General Physics
Observational viability of Herglotz $f(R,T)$ gravity: A multi-probe Bayesian analysis
Vishal M C and Sankarsan Tarai
We investigate the observational viability of the linear Herglotz-type $f(R,T)$
gravity model, $f(R,T)=R+\alpha T$, which incorporates both geometry–matter coupling and non-conservative gravitational dynamics. Unlike previous analyses\cite{Wazny:2025jth} based on illustrative parameter choices, we perform a systematic
Bayesian estimation of the four-dimensional parameter space $\{H_0,A,w,\Phi_0\}$, where $A$ characterizes the matter–geometry coupling, $w$ is the effective equation-of-state parameter, and $\Phi_0$ denotes the present-day Herglotz field. The background
evolution is obtained by numerically integrating the coupled Herglotz cosmological equations at every point in the parameter space. We employ
Cosmic Chronometer (CC), DESI DR2
baryon acoustic oscillation (BAO), and Union3 Type-Ia
supernova data, both independently and in combination. The joint analysis yields $H_0=66.67^{+1.32}_{-1.28}\,\mathrm{km\,s^{-1}\,Mpc^{-1}}$, $A=1.57^{+0.66}_{-0.51}$, $w=-0.992^{+0.136}_{-0.119}$, and $\Phi_0=-0.062^{+0.185}_{-0.157}$ at $68\%$ credibility. The reconstructed Hubble expansion closely follows the flat $\Lambda$CDM prediction over the
redshift range probed by the CC data. However, the deceleration parameter, effective equation of state, $Om(z)$ diagnostic, and statefinder $\{r,s\}$ trajectories exhibit appreciable departures from the concordance model, with the magnitude and
redshift evolution depending on the observational
dataset. These results demonstrate that Herglotz-type $f(R,T)$
gravity can provide an observationally viable description of the late-time expansion while retaining distinguishable cosmological signatures beyond the background Hubble history.
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Atomic and Molecular Clusters · Cross-listed from quant-ph
Programmable Doppler real-space pattern formation in cold atoms
Yijun Tang
Red-detuned Doppler cooling decelerates atomic motion toward zero velocity, whereas blue-detuned light produces acceleration that separates a cold cloud into finite-velocity packets. Here we combine these complementary dynamics in a programmable split-stop protocol for real-space pattern formation. During each stage, a blue-detuned pulse splits existing packets and drives them apart, while a subsequent red-detuned pulse returns their center-of-mass velocities close to zero, results in spatially resolved stationary packets. Repeating this process recursively multiplies the number of packets in real space. We model the dynamics using
stochastic photon-jump simulations that include absorption and spontaneous-emission
recoil in one and two dimensions. Using experimentally realistic parameters for the $689~\mathrm{nm}$ ${}^{1}S_{0}\rightarrow{}^{3}P_{1}$ transition of ${}^{88}\mathrm{Sr}$, we demonstrate an 8-packet one-dimensional array and a 64-packet two-dimensional square array.
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Computational Physics
Inverted model selection in physics-informed neural networks: when a lower residual selects a worse solution
Rabiu Musah
Physics-informed neural networks (PINNs) are commonly evaluated via a single aggregate residual, assuming a smaller residual indicates a better solution. Testing this directly across three constrained PDE systems, I find this assumption can systematically fail. In matched pairs of solvers differing only in whether a defining structural identity is hard-wired or penalized, the penalized variant frequently attains a lower equation residual while violating that identity by several orders of magnitude, causing the exact variant to be falsely ranked worse. Over 64 matched pairs spanning two systems, four network variants, and eight seeds, this inversion occurs in 83% of cases (95% Wilson CI: 72–90%), with rates from 72% to 94% across systems. Testing across six architectures–MLP, cPINN, XPINN, hp-VPINN, and physics-informed DeepONet and FNO–inverts the ranking in 46 of 48 pairs, indicating that this variability is problem-dependent rather than specific to the approximator. A third, larger vorticity–streamfunction problem shows the same ordering: the residual-optimal solver violates its structural identity by over six orders above tolerance, despite a residual margin of only 9.37%. Because a scalar loss cannot expose this, I introduce a lexicographic admissibility gate spanning the structural identity, boundary trace, and a solvability integral that must vanish independently of the equation residual. All three must pass before residuals can compete. This gate catches three artifact classes but misses a fourth: a prescribed-structure prior yields fields that pass every single-run check, yet deleting the source term reveals that 97% of the reported structure survives removal of the physics. Reference data and figures accompany the paper.
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Optics
Simultaneous Intensity and Frequency Control for Optical Waveform Generation using an Acousto Optic Modulator
Hariprasad Madathil, Shengzhi Wang and F. Elohim Becerra
We describe a method for calibrating the response of an acousto-optic modulator (AOM) to enable precise, arbitrary control of the intensity and frequency of optical fields. The method involves characterizing the nonlinear response of the AOM to its input RF drive voltage and applying an iterative calibration/correction
algorithm to accurately map input RF amplitude and frequency drive to output optical intensity and frequency shifts. After a few calibration/correction iterations, the calibration of the AOM maintains the optical power within 1$\%$ of a constant target value over relatively wide frequency tuning range ($\approx$100 MHz), with $\sigma=0.2\%$ relative deviation. We apply this calibration method to generate programmable waveforms and optical pulses with tailored intensity and frequency profiles that closely match their target specifications. This technique offers a simple and robust method for applications that require high-accuracy optical modulation.
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Applied Physics · Cross-listed from cs.LG
Read, Write, Relax: Why Neural PDE Surrogates Need Both Global and Local Processing
Anuj Kumar, Heiko Zimmermann, Josiah Bjorgaard et al. (+4 more)
Recent mesh-based simulation advances have, in no small part, relied on neural surrogates of two distinct families: global models that route information through a small set of latent
tokens, and local models that perform message passing across mesh edges. Consistent with both classes is the inability to perform beyond low-dimensional problems and small-scale or oversimplified meshes, the simulation regimes where industrial problems reside. Our work shows this explicitly and presents a unified formulation. In global approaches, latent-
token attention acts as a spatial low-pass filter, while local message passing lacks the global reach necessary to propagate information across large mesh spaces. Viewed through the error, the two operators are the halves of a multigrid cycle: one corrects errors at the lower end of the spectrum, the other at the higher end, and neither can do the other's job. We introduce Read-Write-Relax (RWR), which interleaves latent
attention with message-passing relaxation under a unified formulation. The interleaved processor lowers error across the entire spectrum, making RWR the most accurate model in nearly every comparison across our industrial and public
benchmarks. It is also markedly data-efficient in the scarce-data regimes, accurate on the engineering quantities of interest, and scales full-field predictions to challenging, large-scale problems.
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All authors Anuj Kumar, Heiko Zimmermann, Josiah Bjorgaard, Jacan Chaplais, Nikolaos Bouklas, Matteo Salvador, Alexander Lavin
Instrumentation and Detectors
Precise Modeling of a Complex Solenoidal Magnetic Field Using a Combination of Analytic Functions and a PINN
Cole Kampa, Susan Dittmer, Henry Glass et al. (+1 more)
We demonstrate an iterative approach to modeling a sparsely measured
magnetic field in a large-bore solenoid. This approach uses a hybrid of traditional and machine learning techniques. The traditional technique is a linear least-squares fit using a series solution to Laplace's equation, while the machine learning technique involves the training of a physics-informed neural network (PINN) on the least-squares fit residuals. We use a newly defined activation function "DELTAsnake," a modification to the snake activation function proposed by Ziyin et al. that allows for stronger
curvature and non-monotonicity. The combined model approximately obeys Maxwell's equations to a level sufficient for producing high quality physics simulations and analysis. Our approach is applied to a highly realistic calculation of the expected
magnetic field in the Mu2e experiment's Detector Solenoid which includes a simple model for the expected statistical measurement uncertainties. Using ten toy measurement simulations, we demonstrate the capabilities of our model in comparison to the least-squares method alone; the least-squares method alone results in a reduced chi-squared statistic of ${2.15 \pm 0.01}$, while our approach improves the reduced chi-square to ${1.034 \pm 0.005}$. Furthermore, for an average toy simulation, we show that the range of the RMS of the three field component residuals reduces from ${0.07-0.37}$ Gauss to ${0.05-0.07}$ Gauss. We find that this novel method is robust against a realistic systematic uncertainty deriving from
Hall probe calibration bias and can be used to significantly reduce the number of measurements required to achieve an accurate model.
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All authors Cole Kampa, Susan Dittmer, Henry Glass, Michael Schmitt
Computational Physics · Cross-listed from cond-mat.mtrl-sci
Attractor-Basin-Limited Fidelity in Reproducible Multistate Vortex Memory
M. Rabiu, S. S. Abukari and M. Amekpewu
Multilevel non-volatile
memory technologies face recurring trade-offs among information density, endurance, retention, and switching energy. We investigate an alternative state variable based on the discrete
vortex configuration of self-organized
vortices in a boundary-driven electron fluid. A dissipative point-
vortex model derived from magnetohydrodynamic dynamics yields six reproducible
vortex codewords, corresponding to 2.585 bits per
cell, whose write fidelity, noise sensitivity, 100-cycle endurance, and effective information capacity are quantified. The ordering of their finite-amplitude basin radii $r_{50}$ differs from that predicted by both fixed-circulation and fully coupled linear-stability spectra. A nonlinear saddle-point construction based on the reduced dynamics likewise does not recover the measured basin ordering. The discrepancy is associated with escape pathways involving coupled position–circulation dynamics that are absent from the fixed-circulation description. These results show that local stability alone does not determine the finite perturbation tolerance of the
vortex states considered here. The proposed
memory requires active hold power, and passive retention remains to be established experimentally.
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History and Philosophy of Physics · Cross-listed from math-ph
How Much Geometry Does Newtonian Dynamics Fix?
C S López-Monsalvo
Newtonian mechanics is usually written on a spacetime of fixed geometry. Here we build that geometry, introducing a propagator and a bilinear form only as the description demands. Within this reconstruction the Principle of Inertia emerges as a
theorem rather than an independent postulate. Inertial motion is blind to the scale of the form and to the torsion the propagator defines, and that blindness is a
geometric precursor of the universality of free fall, before
gravity enters. Newton's Second Law is a compatibility between two derivatives along a curve. The force exceeds the mass times the lowered acceleration by an exact term, and the law survives arbitrary torsion. Requiring force-free motion to be inertial along curves parametrised by arc length restricts the torsion to be totally antisymmetric. The remaining torsion is a three-form which we postulate the particle carries along its worldline. Its contraction with the velocity annihilates that velocity, so the supplementary condition of spinning-particle mechanics holds identically, and we propose that contraction as a classical
spin. The
algebra is familiar from relativistic
spin hydrodynamics; the assignment, to the best of our knowledge, has not been proposed elsewhere. Since inertial motion resolves neither the mass nor the
spin, the predictive content lies in the transport law we postulate, which turns the
spin against a parallel-transported frame at a rate fixed by the acceleration alone and by no coupling constant. That rate becomes a laboratory number only once the arc-length parameter is identified with a clock, which we leave open. The dynamics asks non-degeneracy of the form, so this spacetime is not Newton–Cartan's Galilean one, and gravitation lies outside the construction. We prove every mathematical result, make three constitutive postulates and one physical
conjecture.
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Computational Physics · Cross-listed from quant-ph
Increased cyclicity of atomic transitions via coherent interference of decay paths
Eliza Cornell, Benjamin Pingault, Gergő Thiering et al. (+3 more)
Optical readout is a fundamental tool in atomic state measurement, yet the fidelity of optical readout is frequently limited by imperfect
photon collection. This can be mitigated when readout occurs on a cycling transition which continuously fluoresces under resonant excitation, thus increasing signal and enabling single-shot readout. We present a method to extend the cyclicity of atomic transitions via coherent destructive interference between spurious decay paths. We describe the characteristics of atomic systems in which this method can be implemented and model several examples in which the number of emitted
photons is increased by multiple orders of magnitude.
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All authors Eliza Cornell, Benjamin Pingault, Gergő Thiering, Neil Sinclair, Ádám Gali, Marko Lončar
Physics Education
AI-Augmented Inquiry and Regulation in Hybrid Systems: A Control Allocation Architecture for Preserving Epistemic Agency in Hybrid Human-AI Cognition
Jochen Kuhn, Peter Gerjets, Ulrich Trautwein et al. (+4 more)
Generative artificial intelligence (genAI) systems are increasingly integral to epistemic processes such as hypothesis generation, explanation construction, and decision-making. Although they reliably enhance performance, emerging evidence reveals a metacognitive dilemma: as external generative capacity increases, internal monitoring, calibration, and cognitive engagement may decline. This reflects a redistribution of cognitive control within distributed human-AI systems that cannot be explained by automation bias or reliance on
algorithms alone. We propose the AIRIS (AI-Augmented Inquiry and Regulation in Hybrid Systems) framework to analyze this dilemma and specify where regulatory intervention can counteract it. AIRIS is a multi-level control allocation architecture specifying the conditions under which epistemic agency can be preserved in hybrid generative systems. Drawing on distributed cognition, cognitive load theory, multimedia learning, and self-regulated learning, it identifies seven interacting mechanisms through which hybrid cognition may become destabilized, from delegation and calibration drift to motivational-affective drift. Five regulatory operators (Anticipate, Interrogate, Reflect, Integrate, and Synthesize) target internal generative engagement at points of emerging instability. The architecture does not itself improve learning; it specifies what must remain in place for genAI-supported work to sustain understanding, whether through instructional design, teacher guidance, or learners' own regulation. We derive testable propositions concerning the seven mechanisms and the five operators, reframing AI augmentation as a problem of control allocation in distributed generative systems. Beyond theory, AIRIS offers a research agenda, a design framework for genAI-integrated learning environments, and a conceptual toolkit for the governance of hybrid human-AI cognition.
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All authors Jochen Kuhn, Peter Gerjets, Ulrich Trautwein, Jeffrey A. Greene, Sarah Malone, Patrik Vogt, Tim Fütterer
Optics
Machine Learning to Foundation Models: Artificial Intelligence for Nanophotonic Modeling and Scientific Discovery
Chaobin Yang, Xueqing Liu, Yiqun Fu et al. (+4 more)
Artificial intelligence (AI) is increasingly used to model, design, and study nanophotonic systems. This review traces the development of the field from classical machine learning and deep learning to generative models, transfer learning,
transformers, and emerging foundation models. It first introduces major nanophotonic platforms, including nanoparticles, nanoholes, metasurfaces, photonic
crystals, multilayer thin films, and integrated photonic devices, together with their main forward and inverse problems. It then reviews data-driven methods for predicting optical spectra and fields, generating structures from target responses, improving designs through optimization, and accounting for fabrication constraints. Generative models are discussed as a way to produce multiple valid solutions to nonunique inverse problems, while transfer learning, few-shot learning, and physics-aware training help reduce data requirements and improve generalization. Recent domain-specific foundation models show that different optical structures and responses can be handled within shared representations, but current systems remain limited in scope and physical grounding. Future progress will depend on
multimodal models that connect geometry, materials, spectra, electromagnetic (EM) fields, fabrication data, experiments, and scientific literature with reliable simulation and validation tools. Current foundation models remain domain-specific, and their extension to broader nanophotonic tasks will require stronger physical grounding and validation.
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All authors Chaobin Yang, Xueqing Liu, Yiqun Fu, Fengbo Zhou, Krzysztof Kempa, Stefano Anzellotti, Michael J. Naughton
Plasma Physics
AXUV synthetic diagnostic for ASDEX Upgrade and its application for SPI simulations
Ferenc Lengyel, Weikang Tang, Matthias Hölzl et al. (+14 more)
We introduce an Absolute eXtended UltraViolet (AXUV) diode-based camera forward-modelling tool to support the validation of mitigated disruption simulations and the interpretation of experimental phenomena, with applications to the ASDEX Upgrade (AUG) tokamak. AXUV diodes measure electromagnetic radiation across a wide spectral range with a significantly higher time resolution (~microseconds) than foil bolometers (~milliseconds), albeit with a non-uniform spectral responsivity. AXUV is suitable for examining fast phenomena, such as shattered pellet injection (SPI), where the radiation localisation and radiated power provide information on the deposition of pellet material. Due to the characteristics and degradation of AXUV diodes, absolute power measurements are subject to large systematic uncertainties, especially when the spectra are time-varying, as in e.g. mixed Ne/D2 SPI experiments. These challenges motivated the development of a synthetic diagnostic within the Cherab-Raysect optical modelling framework, which is applied here to four AXUV cameras in two poloidal cross-sections of AUG. The synthetic diagnostic provides a means to understand how the diodes measure radiation under SPI conditions and to connect first-principles
plasma simulations with experimental measurements. The details of the synthetic diagnostic are presented, and the capabilities are illustrated with applications to AUG SPI simulations performed in JOREK. The synthetic signals generated from these simulations are compared with experimental measurements from the 2022 SPI campaign and show qualitatively similar features in many respects. Particularly good agreement was found in the time
evolution of the studied high Ne-content (10%) pellet, whereas a different, low Ne-content (0.17%) case exhibited more pronounced differences, likely due to the absence of background
impurities in the underlying SPI simulations.
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All authors Ferenc Lengyel, Weikang Tang, Matthias Hölzl, Matthias Bernert, Matěj Tomeš, Peter Halldestam, Paul Heinrich, Gergely Papp, Stefan Jachmich, Umar Sheikh, Mathias Dibon, Pascal de Marné, Jörg Hobirk, Thomas Eberl, Gergő I. Pokol, for the ASDEX Upgrade Team, the EUROfusion Tokamak Exploitation Team
Instrumentation and Detectors
Reconstructing High-Fidelity Light Yield Maps for Surface LArTPCs Using Crossing Cosmic Muons
Alex Heindel, Wei Shi, Angelo Ralaikoto et al. (+5 more)
Photon detection systems in
liquid argon time projection chambers provide
prompt scintillation light information that can improve triggering, timing, calorimetry, and interaction reconstruction. These applications require an understanding of the spatial dependence of the detected light yield (LY), which is not adequately described by a single detector-wide average. We present a method for reconstructing voxelized 3D light yield maps in surface LArTPCs using crossing
cosmic muons. For each selected
muon, the path length through every crossed voxel is converted to deposited energy using a minimum ionizing particle approximation, producing a linear system relating the unknown voxel light yields to the total detected
photon signal. The resulting inverse problem is solved using nonnegative least squares, with an additional $L_2$ smoothness penalty used to stabilize weakly constrained voxel values. The method is studied in simulation using the ProtoDUNE-VD detector geometry and
photon detection system. Reconstructed maps recover the dominant spatial structure of a visibility-based truth reference and respond as expected to changes in the
photon detector configuration. Smoothness regularization reduces zero-valued voxels and localized fluctuations while leaving the detector-average light yield approximately unchanged at the selected regularization strengths. The voxel-wise RMSE relative to the truth reference is reduced by approximately 44% for the regularized reconstruction.
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All authors Alex Heindel, Wei Shi, Angelo Ralaikoto, Shuaixiang Zhang, Laura Paulucci, Franciole Marinho, Maressa Pimenta Sampaio, Sanskar Jain
Optics · Cross-listed from quant-ph
Emulating non-Markovian system-bath dynamics with parametrically driven cavities
Valentin Boettcher, Félix Pellerin, Philippe St-Jean et al. (+1 more)
We introduce and characterize a scheme for emulating non-Markovian
quantum system-bath dynamics using the discrete electromagnetic field modes of a parametrically driven cavity. In this scheme, the character of a bosonic bath (the form of the bath spectral density) is tied directly to the shape of the real-time waveform describing periodic parametric cavity modulation. As an explicit example, we demonstrate the feasibility of this scheme for a fiber-loop experiment with currently achievable parameters, supported by numerical simulations and analytical estimates. In particular, we show that a localization transition of the
spin-
boson model can be accurately emulated in this fiber-loop cavity system. This localization effect is characterized by a sharp transition from partial decay to complete decay for a two-level system coupled to a bosonic bath as the bath spectral density $J(\omega)\propto \omega^{s}$ is continously tuned from the sub-ohmic ($s<1$) to the super-ohmic ($s>1$) regime. The transition is only exactly realized in the thermodynamic limit (for an infinite number of bath modes) and for sufficiently weak system-bath coupling. We highlight a competition between the weak-coupling and thermodynamic limits in this problem and show that challenges in approximately realizing the transition can nevertheless be overcome. Finally, we provide bounds on the systematic error introduced when emulating non-Markovian dynamics for arbitrary system observables. The scheme presented here can enable the realization of a modular platform for engineering custom non-Markovian baths, with potential applications in
quantum thermodynamics, thermalization of many-body systems, and resource-efficient
quantum simulations of open
quantum systems.
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All authors Valentin Boettcher, Félix Pellerin, Philippe St-Jean, William A. Coish
Optics
Weyl Points and Fermi Arc Surface States in a Self-assemblable Zinc-Blende Photonic Crystal
Johnathon P. Gales, Hengbin Cheng, David J. Pine et al. (+1 more)
Colloidal self-assembly has long been proposed as a method for growing large-scale three-dimensional photonic
crystals with important optical properties in visible and near-infrared wavelength regimes, where top down lithographic fabrication fails. While much of the focus in these systems has been on producing a photonic band gap, such
lattices can also give rise to topological features of photonic bands. To date, there has been no proposal for realizing topological photonic features in photonic
crystals that may be self-assembled. In 3D, Weyl points are topological band degeneracies that are of particular interest due to their robustness to perturbations and their corresponding
Fermi arc surface states. In order to realize Weyl points, either time-reversal or inversion
symmetry must be broken, and no previous self-assembled colloidal photonic
crystal has exhibited either property. Here, we propose a new zinc-blende structure, built off of recent progress in self-assembling diamond photonic
crystals, which lacks inversion
symmetry and supports photonic Weyl points. Furthermore, we show that the geometry can be optimized to make the Weyl point and its
Fermi arc surface states experimentally observable in the photonic
crystal's projected band structure. Finally, we perform molecular dynamics simulations to demonstrate that the geometry we propose for observing Weyl points is capable of being self-assembled with realistic interparticle interactions. Together, these results provide a platform for the assembly of large-scale photonic
crystals supporting topological degeneracies and robust
Fermi arc surface states in the visible and near-infrared.
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All authors Johnathon P. Gales, Hengbin Cheng, David J. Pine, Mikael C. Rechtsman
Optics
Optical injection locking of white light cavity based superluminal lasers
Jacob Scheuer, Zifan Zhou, Yael Sternfeld et al. (+2 more)
Optical injection locking is a simple method for synchronizing a
laser to an external
laser source by injecting part of the external
laser light into the cavity of the other
laser. This approach received significant interest due to its potential applications in telecommunications, precision metrology, and more. We study and analyze the steady-state and dynamic properties of optical injection locking in white light cavity (WLC)
lasers. We derive the steady-state injection locking range of the WLC
laser and the dynamic (Adler) equation for the phase-difference between the injected signal and the locked
laser output. The analysis shows that WLC
lasers can exhibit an order of magnitude broader locking range and three order of magnitudes faster dynamics compared to conventional
lasers with similar thresholds and free spectral ranges.
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All authors Jacob Scheuer, Zifan Zhou, Yael Sternfeld, Michal Hazan, Selim M. Shahriar
Atmospheric and Oceanic Physics
Probabilistic inference of surface parameters for Monin-Obukhov similarity theory
Ethan YoungIn Shin and Michael F. Howland
In simulations of atmospheric flow, the grid spacing typically exceeds the size of the roughness elements at the surface by an order of magnitude. The unresolved effects of surface morphology and roughness on the flow are represented by effective surface parameters and specified as a surface flux boundary condition, most often through a formulation based on Monin-Obukhov similarity theory (MOST). These surface parameters are known to depend on both surface and flow properties, yet they are generally estimated as deterministic quantities with no characterization of associated uncertainty. In this study, we use a
Bayesian approach to infer surface parameters for MOST and quantify their uncertainties. For the aerodynamic roughness length $z_0$ inferred in isolation, a normal-normal conjugate update yields the posterior and posterior predictive distributions in closed form. We first demonstrate our method on idealized conventionally neutral boundary layers generated by large-eddy simulation, where $z_0$ is prescribed, and quantify how prior- and observation-related choices shape the inferred posterior. We then apply the method to field observations from the Atmospheric Radiation Measurement Southern Great Plains observatory, from which we infer $z_0$ distributions conditioned on month, on wind direction, and on both jointly. By leveraging a prior informed by sample statistics of all near-neutral observations in the training years, we demonstrate the advantage of the
Bayesian inference method for $z_0$ relative to the state-of-practice least-squares profile-fitting method in conditions of data sparsity. Predictions for unseen observations-evaluated at the posterior mean-reduce root mean squared error and mean absolute error in data-sparse wind directions, while posterior predictive distributions consistently reduce the continuous ranked probability score by approximately $20$-$30\%$.
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Computational Physics · Cross-listed from math.NA
A Memory-Efficient Adjoint State Optimization Method Based on Time-Reversible Dynamical Low-Rank Approximation
Lukas Einkemmer and Julian Mangott
The primary challenge of conducting PDE-constrained optimization for high-dimensional problems, such as kinetic equations, is the often prohibitive
memory cost. Computing gradients using the adjoint state method would require the storage of the entire time history of the forward solution. For such problems, where the
memory cost for storing a single instance of the forward solution can already be a limiting factor, this is clearly not feasible. In this paper, we propose a
memory-efficient adjoint state method that compresses the forward and adjoint solution with a dynamical low-rank approximation (a model order reduction technique) and bypasses the need to store the entire forward solution by employing a time-reversible low-rank integrator. The dynamical low-rank approach introduces a number of challenges: reversibility can fail in the rank-deficient case and the low-rank trajectories can show chaotic behavior. In particular, the latter has a number of important consequences for the optimization problem. We address those challenges and show that our method can drastically reduce the
memory requirement for gradient-based optimization of kinetic equations. In particular, we consider two examples from kinetic
plasma physics: optimizing beam profiles to suppress a bump-on-tail instability and shaping a particle beam using external electric fields.
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Optics
Femtosecond Laser Induced Metallization in Silicon via Photon Momentum Mediated Band Transition
Ehatasham Haque, Sunjana Tarannum, Jannatul Shahrin Shoshi et al. (+1 more)
Silicon-based technology has been at the forefront of electronic and photonic research since the dawn of the electronic revolution. However, silicon is fundamentally an indirect-bandgap
semiconductor: the conduction band minimum and valence band maximum occur at different points in momentum space, so optical transitions require
phonon assistance to conserve
crystal momentum. This three-body electron-
photon-
phonon interaction renders silicon an inefficient material for light-absorbing applications such as
solar cells and optoelectronic devices. In this work, we build on an established nanoscale
photon-momentum confinement mechanism, which broadens the
photon momentum distribution and enables
phonon-free absorption in silicon, by subsequently applying a high-intensity femtosecond pulsed excitation that injects hot carriers beyond the Mott density, collapsing the bandgap and inducing a reversible
semiconductor-to-metal transition. While nanoscale
photon-momentum confinement itself has been previously established, the central contribution of this work is this subsequent, carrier density-driven stage, which drives the momentum-enhanced region into a reversible transient metallic phase. Our simulations report highly negative permittivity, enhanced optical power absorption, a large absorption coefficient, and low skin depth, with carrier densities reaching a notable value, far exceeding the Mott threshold. We have shown carrier driven metallization with stable operation below melting threshold of silicon. These results establish a general mechanism for dynamically inducing metallic states in silicon, offering a potential pathway toward monolithically integrated active photonic components and dynamically reconfigurable electro-optical devices within conventional CMOS platforms.
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All authors Ehatasham Haque, Sunjana Tarannum, Jannatul Shahrin Shoshi, Mahdy Rahman Chowdhury
Chemical Physics · Cross-listed from cond-mat.mtrl-sci
Machine-learned exchange-correlation functionals for molecules, solids, and reactive surfaces
Mohamed S. Abdallah, Zhuotao Jin, Boris Kozinsky et al. (+1 more)
The application of density functional theory to heterogeneous catalysis is hindered by the shortcomings of conventional density functional approximations. We combine machine learning with explicitly non-local physically informed descriptors and introduce an exchange-correlation functional (CIDER26SS) framework regularized for wide transferability. CIDER26SS is size-extensive, highly efficient, provides a balanced and accurate description of both molecular and solid-state systems, and is specifically well-optimized for transition metal surface chemistry. Surpassing existing conventional functionals, CIDER26SS resolves the CO/Pt puzzle, identifying the correct binding site for CO adsorption on the Pt(111) surface, along with an accurate adsorption energy, Pt
lattice constant, and surface energy. Predictions agree well with the experimental values, even when all bulk and surface data for Pt are excluded from the training set. Remarkably, CIDER26SS exceeds the accuracy of semilocal approximations even for systems far outside the training domain.
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All authors Mohamed S. Abdallah, Zhuotao Jin, Boris Kozinsky, Kyle Bystrom
Physics and Society
Earth as a Closed Life-Support System: Finite Buffers, Dynamical Thresholds, and Planetary Sustainability
Jean-Pierre Gazeau
We formulate planetary sustainability as a minimal nonequilibrium problem for coupled finite stocks and fluxes, using the operational
language of closed life-support systems: inventories, flux balances, recycling efficiencies, processing capacities, reserve times and failure thresholds. Rather than asserting that waste is universally more limiting than resources, we propose a reserve-time principle: the binding constraint is whichever buffer becomes critical first. A nonlinear model coupling a regenerative resource stock, an accumulated waste stock and an activity variable exhibits a soft threshold, where waste suppresses growth while processing remains formally capable, and a hard threshold, where waste production exceeds maximal processing capacity. With immediate waste feedback, activity settles globally at the soft threshold; the hard threshold is crossed only transiently or under exogenous forcing. A resource-side threshold completes the picture. We extend the model to recycling and to multiple resource and waste classes, give a numerical example, situate the four-function description of life against the origin of life, viruses and aging, contrast the biosphere's two long-term failure modes, and relate the approach to Spaceship Earth, planetary boundaries, Limits to Growth, System Dynamics and stock-pollution economics.
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Data Analysis and Statistics · Cross-listed from astro-ph.IM
NestyNet. IV. Laws Chosen by Nothing in Advance
Rodrigo Ibata, Wassim Tenachi, Foivos Diakogiannis et al. (+2 more)
Differential-equation (DE) discovery tends to break down precisely where much of physics begins. Fields are coupled, governing laws are nonlinear in the state, amplitudes, coordinates, or operators of interest, yet derivatives must remain consistent across fields, channels, and differentiation orders. NestyNet-DE addresses this via two complementary DE search strategies, both employing analytic derivatives from segmented neural surrogates: a sparse-library route linear in the outer coefficients, and a new operator-factorized route that searches directly over equation structure rather than a fixed library, recovering compositional laws the sparse route misses. The recovered laws can be strongly nonlinear in states, fields, coordinates, and their couplings. The framework also handles multi-
dataset shared-support discovery, complex and vector laws, and
Hamiltonian discovery from phase-space trajectories. Beyond a law's form, the same data yield its geometry, the Lie point
symmetries of the recovered equation. We apply the pipeline to 30 years of daily ephemerides of $308$ main-belt asteroids and recover the reduced-Kepler hierarchy (areal law, inverse-square force, and reduced
Hamiltonian), where a discovered rotational
symmetry fixes the centrifugal coefficient rather than fitting it. We also present a
benchmark of 57 real-valued ODEs and 26 complex-valued systems, including the Schrödinger and Dirac equations, with Maxwell's equations as a coupled vector-PDE case study. Here, discovery is not shackled to a fixed library, nor does it end at the equation. It composes laws that no dictionary anticipated, and closes the loop from data, to a law chosen by nothing in advance, to the geometry that explains and integrates it.
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All authors Rodrigo Ibata, Wassim Tenachi, Foivos Diakogiannis, Neil Ibata, Anirudh Shankar
Classical Physics · Cross-listed from math.DG
Braids of Three Strands and Geodesics Shooting in $SL_2(\mathbb{R})$
Jaroslaw Kwapisz
We describe in detail and provide computer code for constructing optimal
geometric braids of three strands from algebraic data encoding the braiding pattern. Our optimality criterion uses the known interpretation of braids as homotopy classes (rel endpoints) of paths in $SL_2(\mathbb{R})$ joining the identity $I$ to some $A \in SL_2(\mathbb{Z})$, i.e., elements of the fundamental group of $SL_2(\mathbb{R})/SL_2(\mathbb{Z})$, a quotient equivalent to the unit tangent bundle of the classical modular surface $\mathbb{H}/PSL_2(\mathbb{Z})$. The main technical result finds the length minimizing geodesic in a prescribed homotopy class. From another perspective, of independent interest, this amounts to shooting the shortest geodesic that connects, with a prescribed number of
spins en route, two given unit tangent vectors to the Poincaré (half-)plane $\mathbb{H}$. The length is measured by a Riemannian metric from a family of deformed Sasaki metrics, sometimes called Kaluza-Klein metrics, whereby unit tangent vectors can be interpreted as infinitesimal rotors, called spinners, and the ratio of the mass to the moment of inertia is the deformation parameter. At the universal covering level $\widetilde{SL}_2(\mathbb{R})$, the geometry is one of Thurston's eight model 3D geometries. In the vanishing mass limit, it converges to the better understood Carnot-Carathéodory contact geometry, where our geodesic shooting extends known formulas. The finite mass case is more delicate, requiring numerical solution of a targeting equation. The characterization of the length minimizing geodesics (No-multiplicity
Theorem) and the resulting targeting equation are the main original contribution. The exposition is complete and multi-pronged, aimed at a broad spectrum of readers. (Numerous figures are the backbone of the narrative and should be viewed in color.)
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Medical Physics · Cross-listed from eess.IV
Multimodal pseudo-CT synthesis for PET attenuation correction using separate modality encoding and topogram conditioning
Rory Bell, Artemis Bouzaki, Jiaming Cao et al. (+2 more)
We participated in the BIC-MAC Challenge with a
multimodal 3D patch-based U-Net for pseudo-CT generation from NAC-PET, MRI, and 2D topograms. By using separate PET and MR encoders, multi-scale feature fusion, and FiLM-based topogram conditioning at the bottleneck, we obtain a model that integrates complementary cross-modal information while reducing reliance on precise voxel-wise correspondence between modalities. Our final submission can be found: https://github.com/rrr-uom-projects/BIC-MAC-MICCAI2026
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All authors Rory Bell, Artemis Bouzaki, Jiaming Cao, Jasmine Morrison, Chelsea Sargeant
Atomic Physics
Magic Velocity Selection in Atom Interferometry
Yuno Iwasaki, Jack Roth, Madeline Bernstein et al. (+2 more)
Velocity-selective Raman transitions are widely used in atom interferometers to prepare atomic ensembles with narrowly defined momentum distributions. However, differential light shifts between atomic energy levels generate velocity distributions that are correlated with the Raman beam intensity, and therefore with the position of the atoms within the
laser beam. We show that these spatially inhomogeneous velocity distributions interact with detuning-dependent systematic effects in a Bragg diffraction-based simultaneous conjugate Ramsey-Bordé interferometer. These interactions can induce systematic phase shifts of order 10 milliradians in the interferometer phase. We further identify a "magic" detuning for velocity selection and show that operating at this detuning suppresses the systematic phase shift. Magic velocity selection eliminates systematic errors arising from correlations between atom velocity and position, facilitating high-resolution atom interferometry experiments targeting sub-part-per-billion accuracy.
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All authors Yuno Iwasaki, Jack Roth, Madeline Bernstein, Andrew Christensen, Holger Mueller
Optics · Cross-listed from nlin.PS
Dynamics of localized solutions in three core coupled waveguides with quasi-periodic nonlinearity
Bruno M. Miranda, Ardiley T. Avelar, Wesley B. Cardoso et al. (+1 more)
In this paper we investigate the behavior of localized solutions, specifically
solitons, in a system of three coupled waveguides. The nonlinearity is modeled by a quasi-periodic modulation influencing the interaction between the waveguides. We analyze the
evolution of the
soliton profiles and their dynamics under varying modulation parameters, highlighting distinct behaviors such as attraction and repulsion among
solitons. Our findings reveal that the system exhibits complex behaviors, depending on the interplay between the quasi-periodic modulation and the waveguide parameters. The study contributes to understanding the impact of quasi-periodic nonlinearity on
soliton dynamics in coupled waveguide systems, laying the groundwork for potential applications in nonlinear optics and photonic devices.
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All authors Bruno M. Miranda, Ardiley T. Avelar, Wesley B. Cardoso, Dionisio Bazeia
Chemical Physics
Machine learning predictions of the Hessian matrix for peptides chains and small proteins
Giorgio Domenichini
Molecular Hessians have a key role in describing molecular vibrations, trajectories and optimization paths. An explicit calculation of them through standard
quantum mechanical methods can be computationally expensive for medium-large systems, and in many applications not even needed. Machine learning methods can be a shortcut to tackle efficiently the computational difficulties. This paper will present a ML model able to predict the Hessian
matrix of biological system made of thousands of atoms. The method, based on learning the Hessian in internal coordinates is intrinsically invariant to molecular rotations and translations, and has a very good scaling with the systems' size. The training was performed on a
dataset of simple aminoacids, as they constitute the building blocks of larger
proteins. From the predicted Hessian
matrix it is possible to calculate thermochemical properties within the harmonic approximations, among them enthalpies,
entropies, Gibbs' free energies, and zero point vibrational energies.
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Chemical Physics
Machine-Learned NMR Shieldings in Molecular Solids with Built-In Hybrid-Functional Molecular Corrections
Matthias Kellner, Ruben Rodriguez-Madrid, Jacob B. Holmes et al. (+4 more)
Fast and accurate chemical shielding
estimators are essential for shielding-driven
Nuclear Magnetic Resonance (NMR) crystallography. Machine-learning models for shielding predictions have matured significantly and today are primarily limited by the electronic structure reference data they are trained on. Here, we introduce ShiftML4, a shielding-tensor model trained directly on monomer-corrected calculations that approximate PBE0, rather than the PBE reference targeted by earlier ShiftML models. ShiftML4 is trained on a diverse set of structures containing 12 of the most common NMR
nuclei in molecular organic solids. On experimental
benchmark sets, the 13C isotropic RMSE against experiment is 1.67 ppm, compared with 2.34 ppm for GIPAW-PBE on the same geometries. ShiftML4 gives a similar 1H prediction RMSE to ShiftML3 (0.5 ppm) and improves the 15N RMSE from 7.24 to 6.08 ppm. The model also reduces errors in the shielding-tensor anisotropy, with an RMSE of 4.63 ppm on 13C CSA principal components against 5.85 ppm for GIPAW. The improvements in prediction accuracy are retained on better geometries. Basing shift predictions on structures relaxed with PET-MOLS, a recent machine-learned interatomic potential that reaches approximate hybrid-DFT geometries in seconds, lowers the ShiftML4 errors further to 0.48 ppm (1H), 1.49 ppm (13C) and 3.66 ppm (15N).
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All authors Matthias Kellner, Ruben Rodriguez-Madrid, Jacob B. Holmes, Pablo A. Unzueta, Gregory J. O. Beran, Lyndon Emsley, Michele Ceriotti
Computational Physics · Cross-listed from cond-mat.str-el
Neural quantum states in condensed matter: advances, best practices, and prospects
Jonas B. Rigo, Björn J. Wurst, Rajah Nutakki et al. (+2 more)
Neural
quantum states provide flexible variational representations of
quantum many-body wave functions by combining neural-network parametrizations with Monte Carlo sampling. In this perspective, we review recent advances in their application to condensed-matter systems, focusing on frustrated
quantum magnets, interacting
lattice fermions, and non-
equilibrium dynamics. We discuss the architectures,
symmetry constraints, optimization methods, and sampling strategies underlying state-of-the-art calculations, and summarize practical guidelines for reliable simulations. We also examine the principal remaining challenges, including learning non-trivial sign and phase structures, controlling variational bias, enforcing physical
symmetries, scaling optimization to large networks, and achieving stable real-time
evolution. Finally, we outline promising directions in which neural
quantum states may extend the reach of classical simulations of strongly correlated
quantum matter.
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All authors Jonas B. Rigo, Björn J. Wurst, Rajah Nutakki, Markus Schmitt, Dante Kennes
Optics
Oblique Bragg-Doppler scattering at relativistic grating created by backward Raman amplification in hydrogen
Wenhong Lai, Jiapeng Huang, Haozhe Guo et al. (+1 more)
We report the first observation of relativistic "Bragg-Doppler" reflection, which occurs when a light beam is incident on a fine-period phase grating moving at close to the speed of light. The grating is created in hydrogen-filled hollow-core fibre by seeded backward stimulated Raman scattering, which creates an intense coherence wave of molecular vibrations at 125 THz, with wavelength 299 nm, moving at ~c/8. When this grating is obliquely probed at a generalized Bragg angle, a reflected beam emerges, Doppler-shifted by 125 THz. The effect is also uni-directional, as phase-matching is strongly violated when the probe beam is reversed, so may be viewed as an ultra-high frequency Bragg-
cell frequency-shifter. The wide transparency window of hydrogen allows frequency shifting of radiation from the visible to the
vacuum UV, simply by tuning the incident angle. The results open up new opportunities for spatio-temporal studies of coherence wave dynamics, frequency conversion in difficult-to-access spectral regions such as the deep and
vacuum UV, and
quantum-state-preserving frequency conversion of single
photons.
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All authors Wenhong Lai, Jiapeng Huang, Haozhe Guo, Philip St.J. Russell