Learning Stiff Dynamical Operators: Scaling, Fast-Slow Excitation, and Eigen-Consistent Neural ModelsStiff dynamical systems represent a central challenge in multi scale modeling across combustion, chemical kinetics, and nonlinear dynamical systems. Neural operator learning has recently emerged as a…Mauro Valorani·Jun 30, 2026SaveLearn
Non-linear control variate in δf particle-in-cell methods using symplectic neural networksWe present a novel δf particle-in-cell (PIC) method for the kinetic simulation of electrostatic plasmas in which the bulk density, acting as a control variate, is evolved using symplectic neural…Victor Fournet, Martin Campos Pinto, Emmanuel Franck et al.·Jun 29, 2026SaveLearn
High-order tensor neural network for iteration-free structure relaxationStructure relaxation is important for the discovery of new materials, yet conventional ab initio optimization remains a major bottleneck in high-throughput screening workflows. Machine learning…Shaobo Yu, Haoting Zhang, Yu Han et al.·Jun 29, 2026SaveLearn
Verified residual-specific explicit derivative kernels for physics-informed learning and discretized PDE adjointsDerivative computation is central to scientific computing, from space-time derivatives in physics-informed neural networks (PINNs) to residual Jacobian actions and discrete-adjoint operators in…Wenbo Cao, Zhe Lu, Weiwei Zhang·Jun 29, 2026SaveLearn
Real-Symmetric Hamiltonian Enables Near-Linear Scaling for Fast Million-Atom Electronic Structure ComputationsThe exploration of quantum phenomena in mesoscale materials, such as moire superlattices, is limited by the cubic scaling cost of conventional electronic structure methods. Here, we introduce a…Zichong Zhang, Shuze Zhu·Jun 29, 2026SaveLearn
Latent Genetic Algorithm for Crystal Structure PredictionPredicting crystal structures requires navigating rugged energy landscapes in which favorable local motifs must be inherited across candidates with incompatible cells, densities, and symmetries.…Kaixin Zheng, Wanjian Yin, Hongyu Yu et al.·Jun 28, 2026SaveLearn
A one-parameter family of realizability-interior closures for odd-order kinetic moment systemsMoment closures at odd truncation order present a fundamental difficulty: the standard Gramian closure saturates the realizability boundary, producing only weak hyperbolicity and failing to preserve…Somdeb Bandopadhyay·Jun 28, 2026SaveLearn
Unit-Circle Moment ClosureMoment closure is a central problem in reduced descriptions of stochastic, kinetic, and quantum dynamics, where equations for low-order observables are coupled to an unresolved hierarchy of…Yu Su, Yao Wang·Jun 27, 2026SaveLearn
Properties of the matrix functions arising in exponential integrators with applications to stochastic simulations of PDEsThe matrix exponential and the closely related matrix phi-functions play a fundamental role in the solution of first-order systems of ordinary differential equations (ODEs). In particular, they…Elliot J. Carr·Jun 27, 2026SaveLearn
Mosaic: A Benchmark Suite for Differentiable Physics SolversDifferentiable partial differential equation (PDE) solvers underpin solver-in-the-loop ML training, gradient-based optimal control, and inverse problems, yet the practical cost of obtaining correct,…Andrin Rehmann, Heiko Zimmermann, Dion Häfner·Jun 26, 2026SaveLearn
Adaptive Probability Flow Residual Minimization for High-Dimensional Fokker-Planck EquationsSolving high-dimensional Fokker-Planck (FP) equations remains a challenging problem in computational physics and stochastic dynamics, due to the curse of dimensionality, unbounded domains, and…Xiaolong Wu, Qifeng Liao·Jun 26, 2026SaveLearn
Real Quantum Chemistry With Complex OrbitalsWe follow up our study of basis set truncation errors for atoms in magnetic fields [Åström and Lehtola, J. Phys. Chem. A, 2023, 127, 10872]. Our previous study employed an approximate real-valued…Hugo Åström, Susi Lehtola·Jun 25, 2026SaveLearn
GPU-accelerated superiorization on constrained physical problems with SupPyThe superiorization method (SM) is situated between feasibility-seeking and constrained optimization. Instead of aiming at the minimum of a given objective function over a constraint set, it seeks a…Tobias Becher, Yair Censor, Kay Barshad et al.·Jun 25, 2026SaveLearn
pyDOF: a Python library for the design of discrete forward and inverse filtersIn this work, we present pyDOF, a Python-based software library which provides a domain-specific framework for the design of symmetric, physical-space, forward as well as inverse discrete filters.…Z. Nikolaou, P. Domingo, L. Vervisch et al.·Jun 25, 2026SaveLearn
A Scalable Time-Based Molecular Dynamics Approach for Simulating Single-Bubble SonoluminescenceWe present a scalable time-based molecular dynamics (TBMD) framework for simulating single-bubble sonoluminescence within a hybrid continuum-MD formulation. Unlike prior event-based approaches, which…Shihan Cheng, David A. B. Hyde·Jun 25, 2026SaveLearn
A convolutional neural network surrogate for hierarchical homogenization: fast elastic moduli prediction of digital rocksDigital rock physics (DRP) aims to estimate effective rock properties (e.g., elastic moduli) directly from 3D micro-CT images. However, direct numerical simulations (DNS) on high-resolution large 3D…Hanfeng Zhai, Rasool Ahmad, Tapan Mukerji et al.·Jun 24, 2026SaveLearn
A Neural Surrogate Approach for Simulating Natural Convection ProblemsThis paper presents a neural surrogate approach for improving the accuracy of natural convection problems simulated with a Boussinesq flow model (incompressible flow with heat transfer). Our…Nurshat Menglik, Alex Shao, David Hyde·Jun 24, 2026SaveLearn
GPU-Accelerated Simulations of Moving Boundary Problems and Fluid-Structure Interaction at Extreme ScalesComputational fluid dynamics and fluid-structure interaction simulations involving moving and deforming bodies is extremely hard. In this work, we present a graphical processing unit (GPU) optimized…Sushrut Kumar, Joshua Romero, Jung-Hee Seo et al.·Jun 24, 2026SaveLearn
Leveraging Population Dynamics to Steer Efficient Search in Large-Scale Combinatorial OptimizationCombinatorial optimization problems pose substantial computational challenges because their feasible solution spaces grow exponentially with problem size. This paper presents a GPU-accelerated…Nikhat Khan, Ridge Redding, Nikhil Shukla·Jun 23, 2026SaveLearn
Machine-learned prediction of carbon interstitial clusters in diamondDiamond hosts optically active point defects central to quantum technologies, yet the carbon self-interstitials introduced during growth and irradiation compete with them and form new defects whose…Xiaoya Chang, Arsalan Hashemi, Nima Ghafari Cherati et al.·Jun 23, 2026SaveLearn
Deep Research in Physical Sciences: A Multi-Agent Framework and Comprehensive BenchmarkDeep research agents are Large Language Model (LLM)-based systems designed for autonomous, multi-step scientific reasoning, and they hold immense potential for accelerating research in the physical…Yigeng Jiang, Tengchao Yang, Taoyong Cui et al.·Jun 23, 2026SaveLearn
Newton's Identity in Finite-Bead Fermionic Partition FunctionFor non-interacting fermions in a harmonic trap, the partition function at any discrete number of imaginary time slices (or beads) and for any choice of short-time propagator admits an exact…A. Chaudhary, J. Valenzuela·Jun 23, 2026SaveLearn
Code-Verification Techniques for Particle-in-Cell Simulations with Direct Simulation Monte Carlo CollisionsParticle-in-cell methods with stochastic collision models are commonly used to simulate collisional plasma dynamics, with applications ranging from hypersonic flight to semiconductor manufacturing.…Brian A. Freno, William J. McDoniel, Christopher H. Moore et al.·Jun 23, 2026SaveLearn
A joint voxel flow-phase field framework for ultra-long microstructure evolution prediction with physical regularizationPhase-field (PF) modeling is a powerful tool for simulating microstructure evolution. To accelerate the simulation of PF models governed by complex PDEs, machine learning methods such as PINNs and…Ao Zhou, Salma Zahran, Chi Chen et al.·Jun 23, 2026SaveLearn
Parallelized contraction of tensor trains or matrix product operatorsTensor Trains (TT), also known as Matrix Product States (MPS) and Matrix Product Operators (MPO), provide a compact and structured representation for high-dimensional data and operators. One of the…Simone Foderà, Marc K. Ritter, Hiroshi Shinaoka et al.·Jun 22, 2026SaveLearn