Papers

This report documents BBun (Blunt Body unsteady), a low-order code for the unsteady potential flow around one or more interacting three-dimensional bodies. The bodies are represented by vortex rings (piecewise-constant doublets) with a Neumann boundary condition; free vortex sheets are shed from the trailing edges and, optionally, from the tips, and convected in time, with an implicit unsteady Kutta condition. The report describes the choices that make the discretization consistent and convergent (a single desingularized kernel per wake line, a bordered factorization of the singular Neumann problem, interior velocity conditions for the nearly singular mode of ring-shaped bodies, the detection of the separation lines from the grid connectivity) and the extensions to several bodies, box wings, symmetric configurations (image method), non-lifting bodies, rigidly rotating bodies (propellers), the unsteady Bernoulli equation, a wake-body collision treatment and a strip-wise integral boundary layer. The verification and validation are reported in full: potential flow around a sphere, sensitivity and grid convergence, the two-dimensional limit, an independent vortex-lattice method, the NACA 0015 wing of McAlister and Takahashi (loads and chordwise pressures), the NACA 0012 profile drag, the biplane of Knight and Noyes, Prandtl's best wing system, Wagner's function and blade-element momentum theory for a propeller. A complete aircraft with counter-rotating tractor propellers illustrates the use of the code.

One of the key unresolved questions in modern cosmology is the Hubble tension, which arises from the inconsistency between determinations of the Hubble constant ($H_0$) based on nearby observations and those predicted by early-Universe data under the standard $\Lambda$CDM paradigm. Recent advances have identified the new Dark Energy Spectroscopic Instrument (DESI) survey as a promising observational tool for probing late-time cosmology. In this paper, we use the latest DESI DR2 baryon acoustic oscillation (BAO) measurements, combined with nonparametrically reconstructed Type Ia supernovae and cosmic chronometer data, to constrain the Hubble constant using three complementary machine learning methodologies: Gaussian process regression (GPR), artificial neural networks (ANNs), and long short-term memory (LSTM) networks. We perform individual and joint constraints on $H_0$ at the effective redshifts of DESI DR2 tracers, yielding $H_0 = 61.8 \pm 5.3\,\mathrm{km\,s^{-1}\,Mpc^{-1}}$ (GPR), $66.3 \pm 5.2\,\mathrm{km\,s^{-1}\,Mpc^{-1}}$ (ANN), and $68.7 \pm 6.0\,\mathrm{km\,s^{-1}\,Mpc^{-1}}$ (LSTM). Our results exhibit excellent agreement with the recent Planck measurements within $1\sigma$, which highlight the potential of data-driven, model-independent methods to address the Hubble tension. Finally, we further test the sensitivity of our results to potential systematics in the DESI data, emphasizing the importance of understanding systematic uncertainties in BAO surveys.

The mechanical wave equation, derived from Newton's laws, takes its simplest form in the rest frame of the medium, and appears to lose that form entirely once expressed in the coordinates of another inertial frame — raising the question of whether, and how, it can be written consistently for an arbitrary inertial observer. This puzzle, often glossed over in undergraduate instruction, is typically set aside by invoking the rest frame of the medium as “preferred,” without clarifying what this means for the principle of relativity underlying physical law. We revisit the wave equation from first principles and demonstrate that this apparent non-invariance stems from its expression in Eulerian coordinates. We present two reformulations: a manifestly invariant form using Lagrangian derivatives applied at the level of microscopic particle force laws, and a factorized Eulerian form separating left and right-moving wave solutions. In this context, we explore limitations of Galilean transformation as the low-speed limit of Lorentz transformation, and discuss potential observational connections, particularly regarding wavelength changes in the Doppler effect. Our derivation provides both conceptual clarity and pedagogical value for intermediate and advanced undergraduate physics.

Chemical reaction-condition optimization – choosing the catalyst, ligand, solvent, reagent, temperature, time, and atmosphere that jointly maximize yield and stereoselectivity – is a central, judgement-laden subtask of organic methodology research that large language models are increasingly expected to support. Yet existing chemistry benchmarks evaluate reaction-class labelling, retrosynthesis, or SMILES manipulation, and do not ask models to read a real condition-screening table and pick the best set. We introduce RxnOptBench, a benchmark whose every option and precedent is a real wet-lab entry mined from the optimization tables of organic-methodology papers published in 2025, graded by a continuous relative score derived from a declared headline utility that combines reported yield with enantiomeric excess (ee), diastereomeric ratio (dr), and regioisomeric ratio (rr), and equipped with a paired precedents-vs-no-precedents design that isolates in-context use of literature evidence from parametric memorization. Across nine frontier LLMs and three Chemistry LLMs, even the best models leave substantial headroom: chemistry-specialized models fall to the random-baseline floor on multi-axis selection, while open-weight models have closed most of the gap to proprietary frontier models. We release the final human-reviewed benchmark test set and evaluation code.

Fast and accurate simulation of hemodynamic behavior within vascular networks is essential for numerous clinical applications. However, obtaining high-quality and computationally efficient flow measurements across complex vascular networks remains challenging. To address this, we first decompose the vascular network into a set of bifurcation units and then develop an operator network capable of mapping unit-specific parameters to the local solution fields of each bifurcation unit. By lumping the Windkessel-model outlet parameters and incorporating inlet boundary conditions from the solution of parent units, the flow and pressure fields can be rapidly approximated. Subsequently, operator-network-driven Schwarz waveform relaxation is applied across bifurcation units to correct discontinuities and improve numerical accuracy. On 7-segment and 55-segment arterial tree models, the proposed method achieves $13\times$ to $17\times$ wall-clock speedups over conventional 1D numerical simulation, with relative $L^2$ errors of 1% in both pressure and velocity. The resulting pulse wave velocity biomarkers agree with the conventional reference to within 1–2%, and the same trained operator generalizes to different tree-like 1D vascular network topologies.

Evaluating the drag coefficient Cd for a sphere moving in a fluid at finite Reynolds number Re has become a classical research subject in fluid dynamics, with publications appearing continuously by numerous authors over the years due to the unavailability of closed-form mathematical solutions. To date, mathematical formulas for calculating Cd as a function of Re can only be obtained by correlation curve fitting to experimental data. The consequence is the coexistence of various formulas with different mathematical fitting functions and an increasing number of fitting parameters. The present study focuses on examining the accuracy of Klyachko's 1934 formula for Cd with two common fractions as fitting parameters, which could enable closed form integration of a simplified equation governing rectilinear motion of aerosol spheres. The computed values of root-mean-square relative error from recently developed formulas show reasonable accuracy of the Klyachko formula for Re less than 1000 and its extension to Re less than 3e5 as a piecewise continuous function, with the possibility of significant improvement through a slight modification. Hence, a few demonstrative examples are presented using the extended Klyachko formula in the study of rectilinear acceleration, the calculation of the terminal velocity of aerosol spheres, and so forth, to shed fresh light on how this classical problem can be studied.

Simplified optical systems often exhibit residual lens aberrations and Veiling Glare (VG), resulting in spatially varying blur and contrast reduction. Large-scale Lens Libraries (LensLib) enable reusable aberration correction models by covering diverse Point Spread Functions (PSFs), but their aberration-only training distribution does not include target-specific veiling glare. Extending such foundations to compound degradation is challenging because realistic target-system compound pairs are difficult to obtain. To address this challenge, we propose LensBridge, a two-stage framework that first establishes a reusable aberration correction foundation and then adapts it to compound optical degradation using only a few unpaired target observations. In Stage I, we build a PSF-aware one-step diffusion foundation by constructing discrete degradation priors from LensLib PSFs and learning to retrieve them directly from aberrated images, enabling PSF-aware correction without requiring explicit PSF at inference. In Stage II, we adapt this foundation to compound degradation through frequency-domain guidance. At the data level, Frequency-guided Degradation Completion (FDC) transfers target low-frequency characteristics to LensLib aberrated images while preserving aberration structures to synthesize compound training pairs; at the model level, Frequency-guided Pseudo Decomposition (FPD) forms aberration- and VG-dominant pseudo observations to condition separate adaptation branches. Extensive experiments across multiple optical systems demonstrate that LensBridge effectively extends reusable aberration correction foundations to joint aberration correction and veiling glare removal without target-system paired supervision. All code will be available at https://github.com/XiaolongQian/LensBridge.

Tetrahymena calcium-binding protein (Tcb2) forms Ca$^{2+}$-responsive networks that exhibit contractile behavior, yet how Ca$^{2+}$ concentration controls their local mechanical response remains poorly understood. Here, we use optical tweezers to perform active microrheology on reconstituted Tcb2 networks inside a microfluidic device that enables precise control of Ca$^{2+}$ concentration, allowing systematic tuning of network structure and mechanics. We find that increasing Ca$^{2+}$ from 1 to 100~mM enhances the effective stiffness by nearly two orders of magnitude, from $\sim$1$\times$10$^{-3}$ to $\sim$7$\times$10$^{-2}$~pN/nm, corresponding to a transition from a viscosity-dominated to a more elastic and mechanically robust network. Recoil assays further reveal rapid release of stored elastic energy following deformation. After two orthogonal pulls, the bead recoils along the diagonal rather than retracing the loading path, indicating that stresses from different directions combine to produce a resultant restoring response. These results establish Tcb2 networks as a minimal, tunable system for probing chemomechanical coupling and viscoelasticity in Ca$^{2+}$-regulated protein networks.

Gas sensor arrays can estimate the composition of gas mixtures from their response patterns. However, when a sensor is used outside the laboratory, the true gas concentrations are unknown, so there is usually no direct way to determine whether the estimated result is reliable. Here, we develop a physics-constrained neural network that estimates time-varying gas concentrations and reconstructs the corresponding sensor signals at the same time. We tested the method with a twelve-channel membrane-type surface stress sensor array exposed to water-ethanol mixtures whose concentrations changed over time. The model reconstructed the concentration profiles of both components from the raw signals. Artificial offsets and sensitivity changes in one channel were detected from disagreement between the measured and reconstructed signals. Deletion or replacement of part of a measurement record was detected from violations of the sorption and mechanical equations. This approach enables time-resolved multicomponent gas analysis with self-verification from a single measurement.

We explore new methods for inducing the movement of water droplets on elastomeric substrates, such as polydimethylsiloxane (PDMS). Our approach builds upon the well-known phenomenon that water droplets move easily across these substrates when swelled w,ith silicone oil. While designing shallow open channels and traps on a PDMS film offers a simple way to control a droplet's trajectory by tilting the substrate in two orthogonal directions, this method is poorly suited for manipulating multiple droplets simultaneously. To address this limitation, we introduce two new methods. The first method utilizes a plano-concave PDMS platform with a radius of curvature significantly larger than the size of the droplet and its translation distance. When the system's configuration is altered, this curved substrate consistently guides the droplet along a curved geodesic. The second method incorporates diode-like gates on the PDMS. These gates are created on either flat or plano-concave PDMS films by embedding thin, rigid plates into the elastomer prior to crosslinking, followed by swelling in silicone oil. This process generates sigmoidal steps that restrict droplet motion to a single direction. Finally, we demonstrate how combining these two strategies enables various forms of directed motion for both single and multiple droplets.

Pickup trucks account for 14% of new light-duty vehicles produced in the United States, yet are among the least aerodynamic. Their open cargo bed adds a flow absent from existing automotive aerodynamics datasets such as DrivAerML and SHIFT-SUV: the shear layer leaving the cab roof passes over a recirculating bed flow before separating again at the tailgate. The resulting drag lowers fuel efficiency, raises emissions and limits the range of electric trucks. Scale-resolved Computational Fluid Dynamics (CFD) is too costly for broad design exploration; neural surrogates can predict flow features at a fraction of that cost, provided they are trained on large-scale, high-fidelity, domain-specific data. We introduce SHIFT-Truck, the first such dataset for pickup trucks. It comprises 1,000 Spalart-Allmaras delayed detached-eddy simulations (SA-DDES) of a reference pickup geometry morphed across 17 shape parameters. Each case is run on a mesh of about 100 million cells at a Reynolds number of $1.4 \times 10^7$ and released with time-averaged surface pressure, wall shear stress, volumetric pressure and velocity. The setup is verified by grid refinement and repeated runs, and checked against wind-tunnel measurements. We define geometry-grouped splits and benchmark four neural surrogates, DoMINO, GeoTransolver, AB-UPT and SMART, on surface and volume tracks. SHIFT-Truck also introduces controlled distribution shifts in the operating point, the input surface discretization and the vehicle archetype. Models with strong in-distribution performance can degrade substantially under these shifts: operating-condition changes expose failures to infer speed dependence, while tessellation and cross-vehicle shifts reveal markedly different robustness across architectures. SHIFT-Truck is thus a benchmark not only for surrogate accuracy but also for generalization across physical and numerical distributions.

We analyze a novel approach for quantum magnetic sensing via optical detection of nuclear spin precession in a noble gas. The detection is carried out with an ultraviolet frequency comb laser, so no alkali vapor is required to be mixed with the noble gas during the sensing stage, allowing for hours-long spin relaxation time at room temperature. Our analysis reveals that combining high laser power and low laser intensity noise is crucial to achieving and exceeding state-of-the-art magnetometric performance, including that of cryogenic magnetometers. We find operational regimes where either fundamental or technical noise dominate, which directs experimental effort towards increasing laser power or lowering laser intensity noise, respectively. We also show that optimizing beam size is key to achieving optimal sensitivity. Our calculations predict that a record-breaking sensitivity of $24~\mathrm{aT}/\sqrt{\mathrm{Hz}}$ is achievable in 10 minutes with a 10 W ultraviolet laser with a relative intensity noise of -100 dBc/Hz.

In a previous work, we explored liquid xenon (LXe)-based positron targets as an alternative to conventional high-Z metallic targets for high energy physics applications. In this paper, we apply computational fluid dynamics simulations to the LXe target and containment vessel, including entrance and exit windows. We design a flow geometry that is able to quickly move heated LXe from previous beam pulses out of the target volume without significant boil-off. While our simulations indicate that the temperature of the exit window can be kept stable, the peak energy deposition density exceeds the widely accepted damage threshold of 35 Jg$^{-1}$ for ILC-like conditions.

Global Dust Storms (GDSs) dominate the Martian climate, yet the mechanisms of dust lifting, transport, and deposition remain poorly constrained because in situ observations are limited: landers are stationary, and their Radioisotope Thermoelectric Generators (RTGs) contaminate local measurements. Our solution is a swarm of lightweight, wind-propelled spherical rovers – Dust Rovers (DRs) – that follow natural wind patterns to collect high-spatial-resolution in situ dust data during storms. Here, we quantify the dispersion and communication connectivity of this robotic architecture. Using 625 wind states calculated using the Mars Climate Database (MCD) for the MY34 storm at the Curiosity site, we build a bivariate-normal model of the near-surface wind velocity and characterise its diurnal structure. Rover trajectories and swarm dispersion are propagated with three integrators: the Euler and fourth-order Runge-Kutta schemes for the deterministic dynamics, and a stochastic Ornstein-Uhlenbeck formulation that simulates turbulent gust forcing. Dispersion is controlled by the unmeasured gust correlation time: with a static rolling threshold enforced, the white-gust case (tau_g = Delta t = 1 s) provides a two-rover separation below 1 m over an hour (0.44 km over a sol), whereas correlation times of 10-60 s yield 6-25 m over an hour (1.4-3.3 km over a sol). We demonstrate that a 20-rover swarm needs only a 1.6-4.1 km per-rover range to remain connected over a sol, and stays connected across the plausible envelope – for gust amplitudes up to twice the nominal value with tau_g <= 60 s, and at the nominal amplitude up to tau_g ~ 150 s.

This note is a technical companion to a previously published preprint describing an Attention Residual U-Net that postprocesses deterministic forecasts from The Weather Company's Global and Regional Atmospheric Forecast (GRAF) model into probabilistic hourly precipitation forecasts. It documents what has changed in that method since publication. Feature-wise Linear Modulation conditioning on calendar season and forecast lead time is used to produce a single trained model for each season, replacing 192 separately trained per-month, per-lead checkpoints. Lead time is extended from 48 to 72 h. Two new input channels are used, per-pixel local solar hour and a static, monthly-varying precipitation climatology. During verification, the climatological reference against which the Brier Skill Score is computed now has an added diurnal dimension, on top of the monthly resolution it already had. Brier Skill Score and reliability are compared between the new vs. the previous training. Forecasts generated with the new training show a modest, consistent improvement of the current training over the original.

We develop a normal-ordered perturbative expansion method to compute the reduced dynamics of a matter system interacting with a general continuous-mode photon field state. In this expansion, all field correlation functions are automatically normal-ordered, while the free evolution is modified by spontaneous emission. Our normal-ordered expansion elucidates the physical meaning of non-normal-ordered correlation terms in the conventional expansion. Furthermore, under intermediate coupling strengths, the normal-ordered expansion converges significantly faster than the conventional expansion, allowing one to extend the applicability of perturbative expansion to much larger matter-light coupling strengths. Interestingly, given an $m$-photon Fock state input, the normal-ordered expansion truncates exactly at the $2m$-th order. For special classes of input states, we show the normal-ordered expansion is consistent with the corresponding master equations. We demonstrate the normal-ordered expansion numerically for a two-level system interacting with coherent state, $m$-photon Fock state, and cat state inputs.

Matter-wave interferometers are sensitive probes of low energy physics and have been used for precise tests of gravity and quantum mechanics. These applications would benefit from interfering particles of higher mass, but observing the interference of a large particle is challenging due to the need to control its initial state and to avoid decoherence. Here we propose to demonstrate matter-wave interference of a microparticle with an embedded rare-earth ion. Optical transitions of the rare-earth ion will impart momentum to the microparticle's center of mass. The use of a time-symmetric interferometer geometry and a rare-earth ion state with angular momentum $J = 1/2$ will eliminate sensitivity to the initial conditions of the microparticle. We show that the rates of all relevant external decoherence mechanisms either decrease or remain constant as the microparticle mass is increased, allowing decoherence to be avoided by using sufficiently large particles. An apparatus at moderate vacuum levels will support microparticle interferometry with up to $10^3$ photons per beam splitter and millisecond coherence time. This demonstration will establish microparticle interferometry as a new platform for quantum sensing, improving searches for minimal modifications of quantum mechanics by up to three orders of magnitude in the near term and laying the foundation for future gravitational tests.

Time-modulated metasurfaces offer a magnet-free route to nonreciprocal wave control, but existing realizations modulate only the electric response of the structure, and proposals for simultaneous electric-magnetic modulation have so far remained theoretical. We experimentally demonstrate a space-time-periodic meta-transceiver whose omega-topology unit cell integrates varactor-loaded electric (dipole) and magnetic (ring) resonators, both modulated by a common unidirectional traveling-wave bias. This modulation gives rise to two independent, experimentally resolved manifestations of nonreciprocity: on transmit, the incident field is efficiently converted into a polychromatic comb of harmonics that radiate at distinct, bias-reconfigurable angles, while on receive, the same modulation instead suppresses frequency conversion and routes the incoming wave preferentially toward one port over the other. These two effects - nonreciprocal polychromatic conversion and unidirectional wave routing - arise from a single unidirectional space-time modulation but act on distinct physical observables, frequency content and spatial direction, respectively. Measured near the electric resonance with a shared bias, these effects characterize the complete cell and do not separate its electric and magnetic contributions. Our results establish the space-time-modulated omega-topology cell as an experimentally viable platform for reconfigurable, full-duplex nonreciprocal wave transformation.

We develop an input-output formulation of quantum light spectroscopy (QLS) by combining the input-output theory, commonly used in quantum optics, with the perturbative expansion method for nonlinear spectroscopy, commonly used in chemical physics. The use of the input-output relation provides physical intuition for how matter systems alter the photon input field. As examples, we consider linear spectroscopy, fluorescence, intra-beam $G^{(2)}$, and Hong-Ou-Mandel correlation. For certain initial photon states, we show that signal expectation values can be evaluated non-perturbatively. For general initial photon states, the signals can be computed via a perturbative expansion of the input-output relation. The input-output relation reduces the work needed to perturbatively expand the optical signal, allowing for cleaner derivations and clearer physical interpretations.

Machine-learned interatomic potentials trained across the periodic table have made atomistic simulation broadly accessible, and specializing them to a single compound class is widely expected to improve accuracy. Yet examples remain scarce, and fewer still surpass universal models in speed or reach a higher level of electronic-structure theory. Here we present a potential that is universal within the class of water-free solid-state hydrogen-bond network mediated proton conductors rather than across chemistry and a database of 4.4 million first-principles configurations spanning 55 materials. It surpasses leading general-purpose potentials across this domain, recovering measured activation energies and the ordering of anion rotational dynamics that those models miss; agreement on static structure does not imply agreement on transport. Accuracy falls for compositions far from the training set but stays competitive for close structural relatives, and a higher level of electronic-structure theory is reached with a few hundred additional configurations per material. We further introduce a compact variant carrying a fifth of the parameters, faster still and yet more accurate than every general-purpose model tested. It sustains more than a quarter of a million atoms on a single graphics processor, placing grain boundaries and the transition into the highly conducting phase within reach, and making a quantum treatment of the protons affordable. Reference accuracy and accessible system size thus become largely independent, offering a template for other compound classes.

Metal-organic framework (MOF) self-assembly mechanisms remain poorly characterized because ab initio molecular dynamics (AIMD) are accurate but too computationally expensive to converge the free-energy surfaces (FES) that govern secondary building unit (SBU) nucleation and growth. This work addresses that limitation for the first step of MIL-101(Cr) SBU formation. Using MACE-POLAR-M, a long-range-aware equivariant machine-learned interatomic potential fine-tuned on a compact DFT reference dataset built from exploratory metadynamics, this work obtains a converged 2D FES for this step at a level of theory ({\omega}B97M-V/def2-TZVPP) at which AIMD would be prohibitively expensive. The pre-trained model samples relevant configurations but gets the thermodynamics wrong, predicting an endothermic reaction and a false global minimum. Fine-tuning corrects both, recovering the expected exothermic process and the correct product basin in agreement with proposed literature mechanisms. The resulting FES also refines the mechanism. It shows that water release from the chromium center proceeds stepwise, through two sequential energy barriers, rather than the single concerted step originally proposed. We find that model accuracy is specific to the reaction coordinate and does not extend to the dissociated fragment configurations excluded from fine-tuning, a direct consequence of the training-data choice that protects accuracy along the reaction path for the FES prediction task. Together, these results establish a validated, computationally tractable route to obtain free-energy surfaces for MOF formation chemistry, transferable to other reactions where AIMD remains prohibitive, and identify training-data composition as the key determinant of which reaction properties a fine-tuned checkpoint can reliably describe.

Active nematics are non-equilibrium fluids composed of rod-like self-propelled units that collectively generate large-scale coherent flows. Here, we focus on a canonical experimental system: an active nematic fluid in $2D$ driven by ATP, composed of densely packed, extended microtubule (MT) bundles cross-linked by kinesin motors. An intriguing feature of this system is the creation and annihilation of topological defects with topological charge $\pm1/2$, due to the fracturing of the material. Experiments confirm that the positive ($+1/2$) defects serve as "virtual stirring rods" that move around each other in a complex braiding pattern. This collective braiding motion of positive defects stretches and folds the fluid itself, i.e., produces macroscale chaotic advection. The degree of self-mixing due to the chaotic advection can be measured using topological entropy and the Lyapunov exponent. Our goal is to determine whether continuum models of MT-based active nematic fluid can reproduce these measurements. To this end, we use two continuum models: the traditional Beris-Edwards (BE) model and the more recently developed Beris-Edwards model with enhanced nematic locking (BENL). The difference between the two models is the adoption of the "nematic locking principle", which states that an individual MT bundle cannot rotate independently of its neighbors due to steric interactions among elongated dense MT bundles. This principle holds in the BENL model except in small localized areas of the material domain where the material fractures, specifically near the creation and annihilation of topological defects. We employ several numerical methods to estimate measures of chaotic advection using both models. Our study shows that the BENL model more accurately reproduces experimental measures of self-mixing driven by chaotic advection in MT-based active nematic fluids.

The Rheological Universal Differential Equation (RUDE) framework embeds neural networks within a frame-indifferent tensorial constitutive backbone and so enables data-driven discovery of complex material rheological behavior. However, the flexibility that makes the RUDE framework attractive also makes it difficult to deploy: the learned neural network correction can make the constitutive equation numerically stiff during training, distribute corrections across non-unique combinations of tensor-basis terms, and extrapolate poorly with increasing nonlinearity, especially for industrially relevant materials with broad relaxation spectra. We develop a stability- and reliability-focused multi-mode RUDE framework that addresses three key challenges: thermodynamic admissibility, out-of-distribution prediction, and interpretability. Logarithmic compression of the input invariants and a split-network architecture improve numerical conditioning during training, while a differentiable projection layer enforces the Clausius–Duhem inequality at every time step. We illustrate the framework with both synthetic (a multi-mode Giesekus model) and experimental data on an entangled silicone polymer melt. Both models were trained on LAOS; they remain stable and accurate under unseen flow conditions, including steady shear, transient stress growth, and extensional flows, for which the unconstrained models may diverge numerically. To aid in interpretation of the contributions of each learned correction to the rheological behavior, we introduce the rheome, a compact way of representing the dominant weighted tensor-basis contributions to the overall rheological behavior. These improvements to the RUDE framework lead to stable and interpretable trained models, paving the way for implementation in computational fluid dynamics simulations and for guiding the rational design of soft material processing operations.

We investigate the dynamics of spherical lipid vesicles subjected to an osmotic shock using continuum simulations of a permeable, area-incompressible fluid membrane with bending elasticity and surface viscosity. The dynamics are governed by a single dimensionless parameter $\varGamma$, which measures the relative strength of osmotic pressure and bending forces. Osmotic forcing drives a three-stage evolution comprising rapid buckling and collapse, smoothing of the resulting wrinkles and ridges, and slow folding into an invaginated morphology resembling the precursor to experimentally observed vesicle-in-vesicle structures. Linear stability analysis predicts the wavelength and growth rate of the initial buckling instability and shows that the dimensionless buckling time scales as $\varGamma^{-2}$, in quantitative agreement with simulations. At longer times, the energy budget reveals a reversal in elastic energy transfer: bending energy accumulated during buckling is released during relaxation and dissipated by membrane viscosity. A scaling analysis of the folding dynamics predicts that the dimensionless folding time scales as $\varGamma^{-2/3}$, again in excellent agreement with simulations. These results provide a continuum description of the mechanisms and widely separated timescales governing osmotically driven membrane collapse and invagination.

Machine learning surrogate models offer a promising path toward accelerating plasma turbulence simulations. We present PreVAE-Turb, a surrogate modeling framework that leverages pre-trained variational autoencoders (VAEs) from the Stable Diffusion image generation model for efficient spatial compression of turbulence fields. The pre-trained VAE is fine-tuned on turbulence data using a physics-informed loss function that includes a spectral loss operating in Fourier space to enforce spectral accuracy across scales. The VAE is combined with convolutional long short-term memory (ConvLSTM) networks to learn temporal dynamics in latent space, with a manifold consistency error metric that monitors encode–decode consistency during autoregressive rollouts. We validate the framework on two-dimensional Hasegawa-Wakatani drift-wave turbulence and extend it to gyrokinetic turbulence from the GENE code, where a four-channel adaptation simultaneously predicts electrostatic potential, density, and parallel/perpendicular temperature fluctuations without requiring architecture redesign. Once trained, inference generates thousands of time steps in seconds on a single GPU, providing substantial computational acceleration compared to direct numerical simulation. The pre-trained approach offers a transferable methodology broadly applicable to various turbulence simulation codes.

Viruses are among the most highly charged objects in biology, and electrostatic interactions permeate nearly every stage of their life cycle. At the same time, an empty icosahedral capsid is almost isotropic as far as the surrounding electrolyte is concerned—its charge distribution is anisotropic only at high multipole order, and electrostatic screening removes such anisotropy long before it can be probed at a distance. Here, we show that the packaged genome of a filled virus imparts a long-range electrostatic anisotropy that extends throughout the surrounding medium, which is not present for empty capsids. We support these findings with a novel Poisson–Boltzmann continuum theory where the orientational order of dsDNA enters through an anisotropic dielectric tensor and an inhomogeneous volume-charge distribution, which is coupled to a charge-regulating shell containing ionizable amino acid residues of the capsid proteins. The resulting electrostatic potential profiles show that DNA packed in an inverse-spool geometry imprints a highly inhomogeneous potential distribution on the outer capsid surface, whose angular structure is a direct result of the DNA-free axial void. At high salinity, the multipole spectrum of this distribution persists at large distances, whereas at low salt concentration, higher-order multipoles decay more rapidly away from the surface. The surviving quadrupole renders virus–wall and virus–virus interactions orientation dependent at the $k_\mathrm{B}T$ level, with a preferred orientation controlled by the sign of the external charge and the pH. Using phage $\lambda$ as a model system and checking robustness of our results on two other phages, we find the same behavior across a range of pH and ionic strengths. This identifies the packaged genome, rather than the symmetry of the capsid, as the origin of the long-range electrostatic anisotropy of a filled virus.

We study transient solute dispersion in pressure-driven micropolar flow through a concentric annulus representing a catheterized artery. The catheter is impermeable to solute, whereas the arterial wall combines irreversible removal with reversible surface retention. The exact steady micropolar velocity field is coupled to the Gill-Sankarasubramanian generalized-dispersion framework; the resulting bulk-surface hierarchy is advanced with Rannacher-damped Crank-Nicolson time stepping to determine $K_0(t)$, $K_1(t)$ and $K_2(t)$. The exchange coefficient is independent of the micropolar parameters and satisfies $-K_0(0^+)=2(\beta+\theta Da)/(1-\lambda^2)$, whereas $K_2-Pe^{-2}\sim\sigma_v^2t$ initially, so the leading short-time dispersion is hydrodynamic and independent of wall kinetics. In the weak-reaction regime, micropolarity modifies convection approximately linearly and shear dispersion quadratically through the velocity-amplitude factor. A narrow-gap analysis with $\varepsilon=1-\lambda$ gives $\bar v\sim(2-N_c)\varepsilon^2/6$ and $K_2-Pe^{-2}\sim(2-N_c)^2\varepsilon^6/7560$ for a fixed pressure gradient, revealing a sixth-power suppression of shear dispersion as the catheter approaches the arterial wall. Increasing $\lambda$ from $0.01$ to $0.30$ reduces effective convection by about a factor of $2.45$, while chemically passive shear-induced dispersion falls by a factor of $24$. The coupled formulation also yields the exact partition $\Phi_m+\Phi_s+\Phi_a=1$, separating mobile, reversibly retained and irreversibly absorbed solute. The reconstructed field quantifies reaction-modulated transverse non-uniformity, while the results distinguish hydrodynamic effects of confinement and microrotation from kinetic wall effects.

Integrated phase-change photonics allows non-volatile optical switching with no static power draw, but the electrical energy needed to reversibly switch the material's state limits practical scaling. Efforts to improve energy efficiency have mostly focused on device optimization, such as redesigning the integrated microheater or waveguide. Here, we take a different, materials-centered approach by alloying Sn to Ge$_2$Sb$_2$Te$_5$ (GST) without altering the device structure or CMOS compatibility. We explore Sn concentrations ranging from 0 at.% to 20 at.% on waveguide-integrated PN and PIN heaters and observe a reduction in both the amorphization and crystallization energies in the 5–10 at.% range. At higher Sn content this trend reverses and the switching energy increases, which is suggestive of phase segregation when combined with TEM compositional analysis. A 10 at.% Sn device continues to switch over 1000 cycles, with observable degradation in the extinction ratio from 4.5 $\pm$ 0.3 dB to 2.4 $\pm$ 0.3 dB. Post-cycling imaging shows PCM migration and void formation which we believe can be further improved by enhanced encapsulation. Overall, Sn alloying offers a route to lower programming energy in integrated phase-change photonics without negatively affecting the switching speed relative to un-doped GST.

We investigate the accuracy and computational efficiency of posterior approximations for gravitational-wave (GW) parameter estimation based on Fisher Matrix and its higher-order extension, the Derivative Approximation for LIkelihoods (DALI), in the context of tests of General Relativity using parameterized deviations in the inspiral waveforms (the TIGER formalism). We adopt the IMRPhenomD waveform and compare the exact posterior sampling to the following approximations in increasing order of complexity: the traditional Fisher Matrix, the Fisher sampling (singlet-DALI), the doublet-DALI and the triplet-DALI. We find that both singlet and doublet-DALI are significantly faster than the exact posterior by factors between 20 and 1000, depending on the inspiral SNRs – larger gains correspond to larger SNRs. These gains are considerably larger for more complete waveforms, such as IMRPhenomHM. To evaluate the accuracy of the approximations, we use Jensen-Shannon divergence. We conclude that both singlet and doublet-DALI systematically improves over the standard Fisher Matrix approximation, while triplet-DALI shows large variability, limiting its practical advantage. We also introduce a code with a new implementation of Fisher and DALI analysis (Symbolic DALI, SymDALI) which computes derivatives symbolically via Wolfram Language.

Two-sample tests are widely applicable in inference and generative modelling, yet users frequently fall back on heuristics and visual inspection due to lack of an accessible test that operates in multiple dimensions. I present Permutation Test using the Energy Distance (PTED), a Python implementation of a powerful two-sample test described in Székely and Rizzo [2004]. The test statistic is the energy distance, a metric on probability distributions that admits a natural sample estimator built from pairwise distances. A permutation test is run on energy distances to produce an exact two-sample test. Because the statistic depends on the data only through pairwise distances, the test applies in high dimensions, on learned feature representations, at large or small or imbalanced sample sizes, and to any data type on which a distance can be defined. An approximation to the formula allows PTED to scale linearly with both number of dimensions and samples while retaining most discriminative power. I highlight a number of instructive sensitivity tests on PTED alongside other multi-dimensional two-sample tests for comparison. PTED is the only method that is sensitive to all tests, though conventional Maximum Mean Discrepancy is identical for all but an over-fitting test.