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Physics Physics Computational Physics

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Thursday 24 September

Computational Physics · Cross-listed from cs.CE

Perspectives on Sustainable Computational Science and Engineering

Julia Kowalski, Ann S. Almgren, Achim Basermann et al. (+43 more)

Computational Science and Engineering (CSE) combines expertise at the intersection of engineering, applied mathematics, and computer science to form powerful methods for model-based design and model-based decision support across disciplines. Today, CSE methods and tools have an impact as an enabling technology in the development of increasingly sustainable products, processes, and operations. However, sustainability is rarely considered holistically in the context of CSE itself. This paper develops a perspective on sustainability in CSE by distinguishing CSE as an enabler of sustainability in application domains from sustainability within CSE itself, which rests on two complementary pillars: sustainable computing and sustainable software. We present illustrative examples for these pillars and derive recommendations and best practices for CSE stakeholders. Developing an understanding of how sustainability can create added value in CSE is an important future direction for the field. This calls for a concerted effort within the CSE community to define measurable outcomes and best practices that embed sustainability as a core design principle rather than an afterthought.
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All authors Julia Kowalski, Ann S. Almgren, Achim Basermann, Alan Correa, Immo Huismann, Uwe Naumann, Hendrik Ranocha, Matthias Rauter, Jens Saak, Michael Schlottke-Lakemper, Robert Speck, Manuel Torrilhon, Mario Wolter, Niklas von der Assen, Maria Guadalupe Barrios Sazo, Dipankul Bhattacharya, Marcel Bock, Faras Brumand-Poor, Dirk Brömmel, Semih Burak, Sara Grundel, Christiane Helzel, Bruce Hendrickson, Robert Klöfkorn, Katharina Kormann, Zahra Lakdawala, Hannah Lanzrath, Camilla Lüttgens, Anna Matuszy`nska, Matthias Meinke, Abigail Morrison, William S. Moses, Georgii Oblapenko, Marcel Pfeiffer, Jana Sasse, Katharina Schmitz, Miriam Schulte, Anna Schwarz, Pit Steinbach, Ingo Steldermann, Anton Stratmann, Lambert Theisen, Joseph De Veaugh-Geiss, Tim Wegmann, Gabriel Wittum, Anil Yildiz

Computational Physics · Cross-listed from gr-qc

Physics-Informed Neural Networks for Static Black-Hole Exterior Metrics: Charge and Cosmological-Constant Sweeps

Huan Jin, Fei Wu and Fei Xue

We apply physics-informed neural networks (PINNs) to recover the time-time component of static, spherically symmetric black-hole exterior metrics from a reduced ordinary differential equation (ODE). Inspired by recent work on solving Einstein field equations with deep learning~\cite{Li2023}, we encode the vacuum/charged exterior through a residual loss and an asymptotic boundary constraint—not by embedding analytic metric terms such as $2M/r$ directly into the network output. Unlike the distributed PINN (DPINN) strategy of Ref.~\cite{Li2023}, which partitions the radial domain into subdomains with separate networks, we employ a unified fully connected network over the entire interval $[r_{\min},r_{\max}]$, avoiding spurious jumps at subdomain interfaces. Moreover, whereas Ref.~\cite{Li2023} restricts training to $r\in(10,300)$—well outside the steep $1/r$ and $1/r^2$ curvature of the inner exterior—we begin at $r_{\min}=10^{-2}M$, spanning three decades in radius and covering the strongly varying region that DPINNs sidestep by domain truncation. Holding the mass fixed at $M=1$, we sweep electric charge $Q\in\{0,0.5,1.0,1.1\}$ at $\Lambda=0$ and sweep $\Lambda\in\{0,0.1,-0.1\}$ at $Q=0.5$. All configurations achieve relative $\mathcal{L}_2$ errors below $6\%$ against the analytic reference. For the representative case $Q=0.5$, $\Lambda=0.1$, three independent trainings with fixed seeds yield relative errors of $3.89\%$, $1.08\%$, and $2.76\%$ (mean $2.58\%$, standard deviation $1.41\%$), demonstrating robustness of the mesh-free approach without labeled field data.
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Computational Physics

CTrex: A Research-Oriented Framework for Kernel- and Projection-Level Algorithm Development in CT Reconstruction

Karel Desplenter, Wannes Goethals, Thomas De Schryver et al. (+3 more)

Micro-computed tomography (micro-CT) is increasingly applied to imaging scenarios in which object motion, deformation, truncated fields of view, continuous rotation, or unconventional scanning protocols violate assumptions underlying classical reconstruction pipelines. Existing reconstruction frameworks efficiently support standard geometries and workflows but offer limited control over how projection operators are discretized and evaluated. This restricts methodological exploration when accuracy depends on ray sampling, interpolation, or operator coupling. We introduce CTrex, a research-oriented, GPU-accelerated iterative reconstruction framework that exposes the projection-correction-backprojection pipeline down to the kernel level. Rather than treating these operators as fixed black boxes, CTrex represents them as editable computational building blocks within a unified iterative structure. This allows non-ideal acquisition effects to be incorporated directly into the numerical operators rather than treated as external corrections. CTrex combines GPU-level accessibility with an extensible geometry formulation based on homogeneous-coordinate view matrices, allowing rigid and affine transformations, detector misalignments, and time-varying geometries to be expressed consistently. It supports circular, helical, offset, and conveyor-belt trajectories while keeping GPU kernels agnostic to the trajectory definition. Applications in motion- and deformation-aware reconstruction, event-based 4D imaging, cylindrical-coordinate reconstruction, and extended-field-of-view CT illustrate how operator-level adaptations enable strategies difficult to realize in conventional frameworks. CTrex provides a flexible platform for CT reconstruction research under realistic and unconventional imaging conditions.
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All authors Karel Desplenter, Wannes Goethals, Thomas De Schryver, Marjolein Heyndrickx, Jan Aelterman, Matthieu N. Boone

Computational Physics · Cross-listed from math.NA

High-dimensional extreme eigenvalue problems: low-rank tensor parametrization and optimization

Pengfei Hao, Renfeng Peng, Yang Kuang et al. (+1 more)

High-dimensional extreme eigenvalue problems often arise from molecular vibrational models, electronic structure calculations, and quantum mechanics. Directly solving these problems suffers from the curse of dimensionality. Instead of tackling the problem in high-dimensional ambient space, we reformulate the problem through low-rank tensor formats, which can significantly reduce the computational cost and storage. Specifically, we consider the Rayleigh–Ritz problem on bounded-rank tensors in the tensor train format, which enables a more flexible choice of rank parameters. Moreover, we consider a smooth parametrization for bounded-rank tensors by introducing slack variables, leading to a smooth manifold structure. The original Rayleigh–Ritz problem is therefore transferred to an optimization problem on the manifold. We develop the Riemannian geometry and propose optimization methods for solving extreme eigenvalue problems. In practice, the Kronecker-product structure in Hamiltonian and PDE operators is employed to simplify the computation. Numerical experiments on the harmonic oscillator, the Laplace operator, the Schrödinger equation, the layered cluster problem, and the Bose–Einstein model demonstrate that the proposed method achieves accuracy comparable to full-space eigensolvers with reduced computation time and avoids storing full vectors. The results also show numerical rank reduction during iteration and robustness when the rank parameter is over-estimated. In addition, the fixed-point iterations illustrate that the proposed methods are able to serve as an inner eigensolver for solving nonlinear eigenvalue problems.
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All authors Pengfei Hao, Renfeng Peng, Yang Kuang, Bin Gao

Computational Physics · Cross-listed from physics.optics

Inverse design of large-scale freeform meta-optics by breaking the memory wall of full-wave simulation

Hyoseok Park and Yeonsang Park

Gradient-based design of large optical surfaces hits a memory wall: a stored-history time-domain adjoint needs $24N_t$ bytes per cell for $N_t$ steps, and every field update is bound by memory bandwidth. Here we present a validated framework that removes this wall and makes globally optimized freeform meta-optics practical. Its finite-difference time-domain adjoint reconstructs interior fields backwards from two recorded planes, so volume memory no longer grows with $N_t$, and fused kernels raise memory-bandwidth use on an H200 GPU from 6.4% to a remarkable 56% of peak. Overlapping tiles, coupled by angular-spectrum propagation and distributed over GPUs, keep tile memory independent of the aperture. Within hours on one GPU node, we optimize, against one global objective, every density variable of single-layer metasurfaces 100 to 200 $\mu$m across, and design a nine-wavelength lens, a color hologram and a polarization-switched hologram. Confirmed by an independent solver within 1.6%, they substantially outperform meta-atom designs, reaching 1.64, 1.65 and 1.24 times their objectives.
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Computational Physics · Cross-listed from nucl-th

Rapid Uncertainty Quantification on a Latent Field using Fisher Information

Karl Daningburg, A. E. Lovell, Arvind T. Mohan et al. (+1 more)

Many inverse problems in physics infer an unobserved field from measurements connected to it through a governing equation. We combine Fisher information with a differentiable solver to quantify uncertainty in both the inferred field and its predicted observables, using a local Gaussian approximation without sampling the full parameter posterior. Applied to nuclear optical potentials, the method distinguishes features constrained by scattering measurements from those whose uncertainty remains set by the prior. Our flexible model improves agreement with held-out cross sections and achieves nearly nominal coverage with narrower predictive intervals than an established Bayesian optical model.
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All authors Karl Daningburg, A. E. Lovell, Arvind T. Mohan, R. O'Shaughnessy

Computational Physics

Monte Carlo sampling of first-order QED processes in laser and pulsar plasmas

Verneri Sarjomaa and Joonas Nättilä

Monte Carlo sampling of strong-field quantum electrodynamics processes underpins simulations of high-intensity laser experiments and of astrophysical compact-object magnetospheres. Sampling an event requires the total rate of the process together with the cumulative probability that determines how energy is partitioned between the produced particles. Simulations typically tabulate both in advance and invert the tabulated probability numerically. Here we replace this procedure with elementary-function approximations for synchrotron radiation and the nonlinear Breit–Wheeler process. For each process, we approximate the auxiliary function that sets the total rate, as well as the cumulative probability, with Padé approximants chosen so that the inversion reduces to a quartic equation. This yields the sampled quantum parameter—electron $\chi_e$ or photon $\chi_\gamma$—in closed form. The approximations and the particle spectra sampled from them agree with the exact results to within $1\%$. The procedure requires no lookup tables, no interpolation, and no numerical root finding, and can be inserted directly into radiative particle-in-cell codes.
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Computational Physics · Cross-listed from physics.chem-ph

Strong-Field-Driven Non-Linear Electron Dynamics in Thiophene Oligomers

Mustapha Driouech, Caterina Cocchi and Michele Guerrini

The interaction between conjugated molecules and intense electric field pulses drives a plethora of intriguing nonlinear phenomena, including optical limiting. While excited-state absorption was recently identified as the primary mechanism for this effect in thiophene oligomers, open questions remain regarding field-dependent population dynamics under intense laser driving. Using real-time time-dependent density functional theory combined with a determinant-overlap population framework, we track the intensity-dependent nonlinear response of a single thiophene ring (1T) and quaterthiophene (4T) as prototypical members of the oligothiophene family with different length and symmetry. We demonstrate that extended $\pi$-conjugation in 4T lowers nonlinear excitation thresholds by orders of magnitude compared to 1T. State-resolved population dynamics reveal complete ground-state depletion accompanied by sequential excited-state absorption channels under intense driving. High-harmonic generation spectra explicitly reflect molecular symmetry constraints, while energy-resolved occupation densities confirm continuous orbital redistribution across the $\pi^*$-manifold. This work provides dynamic physical insights for engineering organic materials with tailored nonlinear optical properties.
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Computational Physics

A Physics-Driven Framework for Parametric Periodic-Flow Modeling and Finite-Amplitude Aeroelastic Response Analysis

Daiwei Dong, Wenbo Cao and Weiwei Zhang

Periodic unsteady flows are common in forced-motion and fluid-structure interaction problems. Their parametric analysis typically requires repeated high-fidelity simulations, whereas existing reduced-order and surrogate models generally rely on pre-generated flow-field or aerodynamic data. This study proposes a purely physics-driven framework for solving parametric periodic flows and finite-amplitude aeroelastic responses. First, a Periodic Physics-Informed Neural Network (P-PINN) is developed to directly solve periodic flows by imposing temporal periodicity over a single motion cycle, thereby avoiding the need to resolve the long transient evolution preceding the establishment of the periodic state. The flow conditions and motion parameters are further incorporated as network inputs to construct continuous parametric representations of the periodic flow field and aerodynamic forces. On this basis, the parametric aerodynamic model is coupled with the structural dynamic equation through first-order harmonic balance to solve the response amplitude and frequency of a single-degree-of-freedom aeroelastic system. This framework is validated using forced-motion cases of a circular cylinder and an airfoil, demonstrating accurate reproduction of periodic aerodynamic forces, surface load distributions, and instantaneous flow fields under different flow and motion parameters compared with time-marching results. Furthermore, aeroelastic analysis is conducted for an elastically mounted circular cylinder at subcritical Reynolds numbers, and the resulting aeroelastic response agrees well with fully coupled CFD/CSD results. Once trained offline, the parametric model can be repeatedly evaluated for different structural parameter states, enabling the complete aeroelastic response curve to be obtained online within seconds, without repeated long-time fluid-structure interaction time marching.
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Computational Physics · Cross-listed from physics.plasm-ph

Prediction of Re-Ignition Times in Dielectric Barrier Discharges

Cristian Flores, Hans Höft, Tomáš Hoder et al. (+3 more)

Discharge ignition events in dielectric barrier discharges (DBDs) self-organise into spatio-temporal patterns with varying degrees of order. The complex dynamics of a DBD and intricate structure of occurring patterns complicate the formulation of predictive, mechanistic descriptions. We present the formulation of a reduced-order model that describes the re-ignition dynamics between consecutive discharges appearing at the same position inside a DBD arrangement. The model is derived from an equivalent electric circuit and validated against fluid-Poisson simulations and experiments performed with a multi-filament arrangement in air-like gas mixtures at atmospheric pressure driven by sinusoidal high-voltage waveforms. The experimental scenarios include a highly ordered regime where discharges ignite at regular time and space intervals generating a pattern stable over several periods, and an unstable regime with discharges appearing at seemingly random positions and times. The model accuracy is assessed in both regimes and it is found that the associated prediction uncertainty provides a quantitative measure of the spatial order of the discharge pattern.
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All authors Cristian Flores, Hans Höft, Tomáš Hoder, Franz X. Bronold, Klaus-Dieter Weltmann, Markus M. Becker

Computational Physics · Cross-listed from physics.chem-ph

Full-frequency GW from Cayley-transformed self-energy moments

Marcus K. Allen and George H. Booth

The dynamical GW self-energy approximation is a key computational tool to provide the fundamental spectrum of electronic systems. We reformulate this approximation, representing the particle and hole parts of the GW self-energy through a highly compact set of Cayley-transformed moment constraints. The Cayley transformation maps real frequencies to the unit circle, keeping the moments bounded as their order increases, ensuring numerical stability and allowing resolution to be focused on an energy range of interest. We calculate these Cayley-transformed moments via an efficient O[N$^4$] scaling contour integration, and from them, construct a Hermitian upfolded Hamiltonian with a linearly scaling dimensionality with system size. A single-shot diagonalization of this effective Hamiltonian gives an explicit full-frequency G0W0 Green's function with manifestly real poles and non-negative spectral weights. This enables quasiparticle energies, satellite features, and their spectral weights to be obtained across the full G0W0 spectrum. Comparisons with exact G0W0 calculations and convergence across the GW100 test set and the larger Chlorophyll A molecule demonstrate substantially faster and more reliable convergence with moment order than an earlier monomial-moment approach. These Cayley moment representations therefore provide a stable, compact, and systematically improvable route to the complete spectral information of zero-temperature GW, without explicit frequency grids, plasmon-pole models and other common approximations, or analytic continuation.
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Computational Physics · Cross-listed from physics.flu-dyn

Wave-driven propulsion of a flexible raft

Elvis A. Agüero and Daniel M. Harris

Inertial propulsion in fluids generally arises from an unbalanced flux of momentum. For the case of wave-driven propulsion, momentum is transported away from an oscillating raft in the form of self-excited surface waves. While this mechanism has previously been analyzed for rigid rafts, the role of flexibility has yet to be investigated. In this work, we develop a fluid-structure interaction model for a periodically driven two-dimensional flexible raft resting at the free surface of a fluid. The raft is modeled as an Euler–Bernoulli beam and is coupled to a weakly dissipative quasi-potential model of the fluid beneath. Varying the flexural stiffness and forcing position reveals new features associated with the introduction of flexibility, including the possibilities of thrust enhancement and reversal. By projecting the raft response onto its free rigid-body and elastic modes, the fluid loading can be represented as a modal impedance, informing a computationally efficient reduced-order model. Analysis of the modal response and its symmetries facilitates physical interpretation of our key findings. For a uniform raft, excitation of any single mode in isolation is incapable of producing a net thrust, and thus efficient wave propulsion requires a blend of interfering modes with appropriately coordinated amplitudes and phases.
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Wednesday 23 September

Computational Physics · Cross-listed from cs.LG

The Mechanics of Delta Learning: Target Design for Generalizable Scientific Machine Learning

Kareem M. Gameel, Ihor Neporozhnii, Sjoerd Hoogland et al. (+1 more)

In scientific machine learning, $\Delta$-learning trains models on residual errors relative to physical baselines, assuming that more accurate baselines with smaller residual scales inherently improve downstream performance. Here, we demonstrate that residual scale alone is an insufficient heuristic for learnability. Evaluating molecular graph neural networks on total energy targets, we show that complex local descriptor baselines can yield small residual targets that are disproportionately rough within architecture-informed proxy spaces and harder to learn relative to their scale. Conversely, semi-empirical baseline reduces both scale and normalized roughness, improving in-domain and out-of-domain prediction. We introduce scale-normalized graph Dirichlet roughness ($D_{\text{IQR}}$) as a pre-training diagnostic for residual learnability and establish baseline complementarity as a core target-design principle, elevating target space formulation alongside model architecture as a key axis for scientific machine learning.
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All authors Kareem M. Gameel, Ihor Neporozhnii, Sjoerd Hoogland, Oleksandr Voznyy

Computational Physics

Conditional preservation of chimera states under equitable network coarse-graining

Margherita Bertè, Thierry Njougouo, Paolo Sarti et al. (+1 more)

Chimera states, characterized by the coexistence of coherent and incoherent dynamics in networks of coupled oscillators, are among the most intriguing collective phenomena in nonlinear systems and are strongly shaped by the underlying topology and initial conditions. Their analysis in large-scale networks remains computationally demanding and motivates the development of coarse-graining strategies that reduce network size while retaining the essential dynamical features of chimera behavior. In this Letter, we investigate whether equitable-partition-based quotient graphs can preserve chimera patterns under substantial reductions of network size. Using nonlocally coupled FitzHugh-Nagumo (FHN) oscillators, we exploit the fact that, for an equitable partition, the full-network dynamics restricted to the associated cluster-synchronous subspace coincide exactly with the quotient dynamics. We ask whether this structural exactness is sufficient to preserve chimera signatures after the network size reduction. Our results show that this is not necessarily the case: agreement between quotient and full-network chimera dynamics depends on the equitable partition, and exact quotient dynamics do not necessarily guarantee the transverse stability of the corresponding full-network trajectory. Importantly, when the cluster-synchronous solution becomes transversely unstable, the full network may depart from the exact quotient trajectory while remaining in a macroscopic chimera regime. Therefore, structural exactness and dynamical preservation are distinct requirements, and transverse stability provides a complementary criterion for assessing quotient-based coarse-graining of chimera states.
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All authors Margherita Bertè, Thierry Njougouo, Paolo Sarti, Tommaso Gili

Computational Physics · Cross-listed from astro-ph.IM

Differentiable astrophysics at scale: solving and differentiating ODE ensembles on the GPU

A. Spurio Mancini

Astronomers increasingly fit their models with gradient-based methods, such as Hamiltonian Monte Carlo, which need the derivatives of the model with respect to its parameters. In many analyses a prediction requires solving a small system of ordinary differential equations (ODEs) for thousands to millions of parameter sets, and this ensemble of integrations often sets the cost of the analysis. We introduce to the astronomical community GRADSOLVE, a JAX library that moves this computation to graphics processing units (GPUs): it integrates each member of the ensemble in its own GPU thread with its own step size and returns the derivatives with respect to the parameters in the same pass. We measure the speed-up it provides in three examples from different fields of astronomy, stellar orbits in the Galactic potential, the expansion history of a dark-energy cosmology and the spin precession of binary black holes, with every code held to the same accuracy requirement. For a million trajectories GRADSOLVE runs 8.5 to 1500 times faster than the serial CPU code of each example running on all 128 cores of a CPU, and several orders of magnitude faster than on one core. On the same GPU it is also 11 to 15 times faster than DIFFRAX, the state-of-the-art ODE library in JAX, in all three examples. With GRADSOLVE a million such integrations take seconds or less on one GPU, so gradient-based analyses that need ensembles of this size become routine. The code is publicly available at https://github.com/ECLIPSE-AI4Science/gradsolve.
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Computational Physics · Cross-listed from astro-ph.HE

Performance-portable GPU acceleration of the hybrid particle-in-cell code dHybridR

Bricker Ostler, Miha Cernetic and Damiano Caprioli

Hybrid particle-in-cell simulations are widely used to study kinetic processes in collisionless astrophysical and space plasmas, yet the high computational cost of large-scale three-dimensional runs has largely confined production studies to two dimensions or restricted domains. To address this challenge, we present a performance-portable GPU implementation of the hybrid particle-in-cell code dHybridR. The implementation combines OpenMP target offloading with specialized SYCL kernels for the most computationally expensive operations, while preserving a unified CPU-GPU codebase that supports Intel, AMD, and NVIDIA GPUs. On the exascale supercomputers Aurora and Frontier, dHybridR achieves weak scaling efficiencies of $86\%$ to $97\%$ across 49,152 accelerators, and at 256 particles per cell, its full-node GPU throughput is up to $32\times$ that of the vectorized CPU implementation at approximately $90\%$ less energy per particle-update. To our knowledge, no other hybrid particle-in-cell code has reported GPU performance at this scale, leaving dHybridR uniquely positioned to exploit exascale systems. These advances substantially lower the computational barrier to large-scale three-dimensional hybrid-kinetic simulations of collisionless plasmas.
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Computational Physics · Cross-listed from physics.chem-ph

Simulation of a Battery Cell on Quantum Computers: Reactions & Transport

Albert J. Pool, Michael Schelling and Birger Horstmann

Simulations of electrochemical materials and systems accelerate technological progress, but are still limited by computational power. In particular, quantum computing offers prospects for higher resolutions, due to the exponential amount of data that can be stored in a quantum state. As current quantum computers are still noisy, we consider a hybrid quantum-classical algorithm, that divides the problem into smaller computational tasks. We describe how to implement such an algorithm for non-linear partial differential equations, the Feynman-Kitaev Hamiltonian, in a scalable way for an electrochemical system. We show how it can evaluate general electrochemical models and present a quantum simulation of the Single Particle Model with electrolyte (SPMe) as the first quantum simulation of a battery cell.
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Computational Physics

Optimal limits on weak integrability breaking and protected thermal memory near qutrit exchange

Hanbing Liang and Fujun Liu

Although integrability does not universally require a continuous one-site symmetry, we rigorously prove that every jointly analytic, regular Yang-Baxter deformation of the qutrit exchange interaction necessarily retains a nontrivial, analytically varying one-site charge. Breaking this local symmetry imposes a fundamental physical constraint on approximate conservation, governed by the optimal uniform bound $\delta^3 \le C\varepsilon$ that explicitly relates the minimal one-site symmetry defect $\delta$ to the local current-conservation residual $\varepsilon$. While breaking all one-site charges strictly forbids an exact integrable completion, an optimally compensated nearest-neighbor interaction saturates this cubic limit and anomalously extends the guaranteed infinite-temperature energy-current correlation window to order $|\lambda|^{-3}$ in the perturbation strength $\lambda$. Furthermore, we reveal a fundamental resonance obstruction for intrinsic conversion perturbations that strictly prevents any exact first-order repair of a broken one-site charge on any finite ring. Nevertheless, we demonstrate that the complete eight-dimensional charge memory matrix remains thermodynamically protected and approaches the identity for timescales $t=o(|\lambda|^{-3/2})$, a robust feature of the full infinite-temperature dynamics when the thermodynamic limit is taken before weak coupling.
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Computational Physics · Cross-listed from cond-mat.stat-mech

Optimal Evasion from a Resetting Searcher

Ami Taitelbaum, Matan Shporer, Shlomi Reuveni et al. (+1 more)

Returning to a familiar location can help a searcher find its target—but how should the target move to avoid being found? We address this question for a resetting Brownian searcher and a mobile target fluctuating around a home location. Keeping the target's typical exploration range fixed, we find that the mean capture time is maximized at an intermediate correlation time: rapid motion promotes encounters, whereas slow motion leaves the target effectively stationary. Between these extremes, persistent excursions away from the searcher's reset point can substantially delay capture. Analytical limiting results explain this optimum, while numerical solutions reveal an approximately linear relation between the maximal mean capture time and the optimal correlation time across many decades. These findings identify temporal correlations as a resource for evasion, allowing a target to prolong survival by changing how it moves rather than where it roams.
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All authors Ami Taitelbaum, Matan Shporer, Shlomi Reuveni, Michael Assaf

Computational Physics · Cross-listed from cond-mat.mtrl-sci

SoLiD26: A First Principles Solid-Liquid Interface Dataset for Machine-learned Interatomic Potentials

Jonas Busk, Emil J. P. Frost, Yogeshwaran Krishnan et al. (+7 more)

Machine-learned interatomic potentials (MLIPs) for solid-liquid interfaces in advanced materials applications, e.g., electrochemistry, catalysis and corrosion, require training data that samples both liquid environments, the solid and the interface itself. We present SoLiD26, a curated solid-liquid interface dataset, containing 15.4 million first-principles atomic structures with up to 576 atoms and 15 chemical elements for training and evaluating MLIPs. The structures were compiled from density functional theory (DFT) calculations performed in studies of solid-liquid interfaces, with most configurations originating from ab initio molecular dynamics (AIMD) simulations. Each record contains atomic species, positions, simulation cell, periodic boundary conditions, potential energy and atomic forces. SoLiD26 includes aqueous coinage metal interfaces, electrode-electrolyte systems, and selected bulk reference structures, calculated with VASP using the PBE functional and D3 dispersion corrections. We describe the data ingestion and preparation pipeline used to construct the dataset. The application of SoLiD26 for training and evaluating MLIPs is demonstrated with a suite of MACE models on a simple training, validation and test split. The dataset enables development and benchmarking of MLIPs for structurally and chemically heterogeneous solid-liquid interfaces.
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All authors Jonas Busk, Emil J. P. Frost, Yogeshwaran Krishnan, Henrik H. Kristoffersen, August E. G. Mikkelsen, Xueping Qin, Xin Yang, Heine A. Hansen, Arghya Bhowmik, Tejs Vegge

Computational Physics

Optimal Bias Potentials via Ergodic Optimal Control and Generator Learning

Lei Guo, Carsten Hartmann, Thomas Berger et al. (+1 more)

We investigate the computation of optimal bias potentials for accelerating transitions between metastable states and for computation of equilibrium properties in molecular dynamics simulations. We formulate optimal biasing as an ergodic optimal control problem (OCP), which can be recast as a linear eigenvalue problem for the infinitesimal generator of the unbiased dynamics. We demonstrate that data-driven learning methods for the generator enable reliable solution of the OCP, computation of biasing potentials, extraction of equilibrium properties, and acceleration of state transitions. We also explore the relation of the control problem to coarse grained representations and learning of coarse grained dynamics.
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All authors Lei Guo, Carsten Hartmann, Thomas Berger, Feliks Nüske

Computational Physics · Cross-listed from physics.optics

Inverse-Designed Chiral Metasurfaces for Enhanced Helical Dichroism

Chia-Chun Pan, Yu-Hsi Chen, Munseong Bae et al. (+5 more)

Helical dichroism (HD), arising from the interaction between structured light carrying orbital angular momentum (OAM) and chiral matter, offers opportunities for enhanced chiroptical characterization at the subwavelength scale. Here, we employ inverse design to develop chiral metasurfaces tailored for enhanced HD under Laguerre-Gaussian (LG) beam illumination. The optimized planar structure exhibits a simulated HD around 115% at 800 nm for opposite OAM states, revealing the strong chiroptical response enabled by the inverse-designed geometry. The optimized metasurfaces were fabricated from Si$_3$N$_4$ and experimentally characterized using a custom-built optical system with controlled OAM excitation. A single chiral metasurface exhibited a maximum experimental HD up to 62% near 800 nm, demonstrating a strong OAM-dependent helical dichroic response from a single 6-micron-large chiral structure. Although the measured HD value is lower than the optimized simulation value, the experimental result validates the feasibility of inverse-designed chiral metasurfaces for structured-light chiroptics. These findings establish inverse design as an effective approach for engineering compact chiral nanophotonic structures with enhanced interactions with structured light, offering potential for miniaturized chiroptical sensing and OAM-resolved imaging.
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All authors Chia-Chun Pan, Yu-Hsi Chen, Munseong Bae, Haejun Chung, Dan Smith, Daniel Fan, Sejeong Kim, Ranjith R Unnithan

Computational Physics · Cross-listed from cond-mat.stat-mech

Multiscale Entropies as Order Parameters for Nonequilibrium Phase Transitions

Gerhard Jung and Misaki Ozawa

We develop a multiscale entropy framework based on the wavelet conditional renormalization group that decomposes the total Shannon entropy into contributions from different spatial scales. Generative modeling enables accurate estimation of these entropies from configurations or snapshots, even in nonequilibrium systems whose underlying probability distributions are unknown. Applications to the equilibrium $\varphi^4$ model and nonequilibrium active Model B+ show that multiscale entropies reveal criticality and spatial organization obscured in the total entropy. These entropies serve as scale-dependent order parameters for characterizing phase transitions and pattern formation.
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Computational Physics · Cross-listed from cs.AI

SHRAV: State-Hypothesis-Reason-Action-Verify Framework for Physical Modeling and Inverse Design

Ziheng Guo and Yang Bu

Physical modeling and inverse design require computation that can continue from reusable state. We introduce SHRAV, an architecture-independent computational framework organized around State, Hypothesis, Reason, Action, and Verify. Its central mechanism is a state-continuation core with declared reuse boundaries and explicit roles for learned evolution and numerical quantities. Forward configurations evolve predictive state and read out physical responses; inverse-design configurations additionally generate target-directed modifications and consume evaluator feedback. Electromagnetic world-model studies are mapped to forward configurations, with selected readout and reuse diagnostics reported here. Computational lithography demonstrates an inverse-design configuration: four fixed-weight design updates improve thresholded aerial-image intersection-over-union from 0.5313 to 0.8153 under independent scalar-pupil replay, with a maximum absolute IoU difference of approximately 0.000824 between predictor estimates and independent replay.
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Computational Physics · Cross-listed from physics.flu-dyn

Concentration of Stationary Measures onto Large Scales in the Fast-Advection Limit of the Stochastically Forced Two-Dimensional Navier-Stokes Equations

Tobias Rohner

We investigate the fast-advection limit ($\varepsilon \to 0$) of the stochastically forced two-dimensional incompressible Navier-Stokes equations on the torus. Numerical simulations are performed at resolution $N = 128$ for four different forcing shells on both the square torus and a thin torus, with the advection parameter $\varepsilon$ varied over approximately one decade. We find that the stationary distributions become increasingly concentrated on the lowest Fourier modes as $\varepsilon \to 0$, with the energy-to-enstrophy ratio $E/\Omega$ and the fraction $R$ of enstrophy in the lowest modes both increasing monotonically. At finite $\varepsilon$ the degree of concentration depends on the forcing shell through the effective spectral value $B_1/B_0$, in agreement with the condensation bound of Sznitman and Widmayer. Linear extrapolation to $\varepsilon = 0$ suggests that the limiting values may be forcing-independent, with $B_1/B_0$ governing only the rate of convergence. The same qualitative behaviour persists on the thin torus.
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Tuesday 22 September

Computational Physics · Cross-listed from cond-mat.str-el

Exact Equivariance from Ordinary Neural Networks for Lattice Many-Body Dynamics

Ho Jang, Sankha Subhra Bakshi and Gia-Wei Chern

Large-scale simulations of correlated electron systems require repeated evaluations of the electronic forces and transition energies driving collective dynamics. Machine-learning surrogates alleviate this bottleneck, but incorporating symmetry often involves carefully designed descriptors or specialized network architectures. We show that ordinary multilayer perceptrons equipped with finite-group averaging provide exactly equivariant surrogates directly from microscopic configurations. The construction separates symmetry enforcement from the internal network architecture and applies to both discrete and continuous lattice degrees of freedom. In the Falicov–Kimball model, it predicts directional hopping free-energy differences; in the Holstein model, its invariant limit generates conservative lattice forces. Benchmarks against exact diagonalization establish microscopic accuracy and agreement of dynamical correlations, while large-scale simulations recover multiscale charge ordering and charge-density-wave coarsening. These results demonstrate an accessible, reusable route to symmetry-preserving many-body dynamics for systems with finite lattice point groups.
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Computational Physics · Cross-listed from physics.med-ph

Towards Modeling the Hemodynamic Impact of Mitral and Aortic Valve Repair in Patients with Left Ventricular Assist Devices

Mia Bonini, Michael Ferguson, Marc Hirshvogel et al. (+4 more)

Valve dysfunction is a major threat to long-term success in left ventricle assist device (LVAD) therapy, with direct implications for right heart performance. In this study, we apply a patient-specific, image-based computational modeling framework to evaluate the hemodynamic impact of simulated mitral and aortic valve repair in five LVAD-supported patients. Each patient was modeled under four conditions: (patient-specific LVAD-supported state), simulated mitral valve (MV) repair, simulated aortic valve (AV) repair, and simulated combined MV&AV repair. Because validation data for valve repair were unavailable, the simulated repair scenarios are exploratory in silico interventions based on clinically validated patient-specific models. The models integrate dynamic CT imaging, echocardiography, catheterization data, and device-specific LVAD parameters into a coupled 3D-0D simulation pipeline. Valve dynamics are governed by transvalvular pressure and flow, allowing physiological modeling of regurgitant lesions and surgical repair. The right ventricular (RV) was assessed using a combination of model-derived metrics, including right ventricular ejection fraction (RVEF), pulmonary artery pulsatility index (PAPi), and RV-PA coupling. In addition, we performed blood residence time (RT) analysis to evaluate blood stasis within the left heart and aorta. The simulations suggest that valve repair improved cardiac output, reduced pulmonary congestion, and enhanced right ventricular loading conditions. Notably, mitral valve repair restored aortic valve opening during the cardiac cycle, which improved sinus washout and reduced blood residence time within the aortic root-factors associated with lower thrombotic risk. Overall, these findings suggest a potential role for valve repair in LVAD-supported hearts, though larger, validated patient cohorts are needed to confirm these results.
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All authors Mia Bonini, Michael Ferguson, Marc Hirshvogel, Maximilian Balmus, Paul C. Tang, Francis Pagani, David Nordsletten

Computational Physics · Cross-listed from physics.geo-ph

Distributed Proximal Stein Variational Gradient Descent Algorithm for Large-scale Bayesian Inference in Traveltime Tomography

Akshay Vishwakarma, Kamal Aghazade, Ali Siahkoohi et al. (+1 more)

We present a distributed framework for large-scale Bayesian inverse problems governed by the eikonal equation, with a specific focus on seismic traveltime tomography. Traditional deterministic approaches often fail to provide the uncertainty quantification (UQ) necessary for ill-posed problems, while conventional Bayesian sampling methods such as Markov chain Monte Carlo (MCMC) suffer from the curse of dimensionality and slow convergence in high-dimensional model spaces. The proposed framework addresses these challenges through a three-tier computational strategy. First, we utilize the Fast Marching Method (FMM) to solve the eikonal equation, ensuring high numerical accuracy. Second, we reformulate the global tomographic objective into a decentralized consensus form, allowing the inversion to be decomposed into independent subproblems solved in parallel via the Alternating Direction Method of Multipliers (ADMM). This architecture eliminates the need for the explicit construction of large-scale sensitivity matrices, significantly reducing the memory footprint for 3D surveys. Finally, we integrate Stein Variational Gradient Descent (SVGD) within the ADMM workers to perform approximate posterior sampling. By evolving a set of model particles along a functional gradient direction that balances data-fitting forces with a repulsive kernel-based diversity force, we obtain an ensemble from which posterior summaries are computed. We derive a data-space Gauss-Newton update using the Woodbury matrix identity to further accelerate the particle evolution in large-scale 3D problems. Numerical experiments on complex 2D and 3D models demonstrate that the algorithm achieves stable convergence, produces high-fidelity velocity reconstructions, and provides posterior uncertainty maps.
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All authors Akshay Vishwakarma, Kamal Aghazade, Ali Siahkoohi, Ali Gholami

Computational Physics · Cross-listed from math.NA

Uniform approximation of spectra of linear second order differential operators via discrete sine transform based discretisation

Oliver Křenek, Vít Průša, Rebecca Tozzi et al. (+1 more)

We study various discretisation schemes for the regular Sturm–Liouville operator on a bounded interval and for the Laplace operator on an arbitrary planar domain, in both cases subject to zero Dirichlet boundary conditions. The objective is to identify a discretisation scheme such that the corresponding discretised operator—a matrix of size $N \times N$—produces $N$ eigenvalues that approximate as closely as possible the first $N$ eigenvalues of the corresponding operator at the continuous level. By means of numerical experiments we examine several conventional discretisation schemes, and we corroborate the known fact that the conventional discretisations fail to achieve the objective, with the failure attributable to the poor approximation of high-index eigenvalues, and, as a result, to the non-uniform spectrum approximation. In contrast, the newly proposed discrete sine transform based discretisation scheme is designed in such a way that it replicates the asymptotic behaviour of high-index eigenvalues, thereby providing the sought uniform spectrum approximation. Of equal significance is the fact that the proposed discrete sine transform based scheme can be, unlike many Fourier transform based methods, applied to non-rectangular domains.
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All authors Oliver Křenek, Vít Průša, Rebecca Tozzi, Martin Vejvoda

Computational Physics

Learning continuous reaction paths for transition-state prediction

Yexiang Yang and Linlin Zhong

Transition states are defined by reaction pathways, yet most machine-learning methods predict them as isolated geometries. We introduce MARC-TS, a two-stage framework that learns a continuous, endpoint-conditioned path, queries it at any resolution and uses local path context to refine a transition-state candidate. We construct T1x-IRC-8K, a dataset of 8,209 reactions and 1,088,725 path-resolved geometries. On held-out reactions, the path model reduced complete-path error by 48.4% relative to endpoint interpolation, and the localizer achieved a mean aligned structural error of 0.127 Å. Quantum-chemical optimization and vibrational analysis yielded 405 frequency-confirmed first-order saddle-point candidates from 410 predictions. In a 100-reaction nudged elastic band comparison, learned-path initialization reached a joint geometry-and-force target for 66% of reactions, compared with 12% for geometric interpolation after 100 optimizer steps. By treating the path as a reusable representation rather than an auxiliary output, MARC-TS connects transition-state prediction, mechanistic interpretation and quantum-chemical refinement.
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