General & Applied Physics · Physics & Astronomy
Computational Physics
The newest papers in Computational Physics that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
244 papers, from 31 August 2026 to 5 November 2026: 204 arXiv preprints and 40 OpenAlex records.
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Newest papers, page 6
FoundPDE: A generative foundation model for data-driven symbolic discovery of partial differential equations
Discovering governing partial differential equations (PDEs) from observed data is a central inverse problem in computational science and engineering: the goal is not only to predict trajectories…The importance of tensor handling and implementation of physics-informed DeepONets
Physics-informed neural networks (PINNs) reduce or eliminate the need for large labelled datasets by enforcing governing equations during training.An Adaptive-Horizon Graph Neural Operator for coupled surrogate morphodynamic modelling
Morphodynamic simulations require repeated coupling between hydrodynamic solvers, sediment transport modules, and bed updating.Learnable Splines for Unstructured Neural Operators
Neural operators approximate solution operators of partial differential equations at a fraction of the cost of numerical solvers.Transient Heat Conduction on Quantum Hardware: Replicated Temperature-Field Reconstruction and Shot-Count Sensitivity with a Variational Quantum Linear Solver
Implicit discretizations of transient heat conduction repeatedly generate sparse linear systems, making diffusion problems a natural test bed for quantum linear solvers.Deep Mesoscopic Dynamics
Microscopic simulations resolve spatial patterns but are feasible only over small domains and short timescales, whereas bulk descriptions erase those patterns through spatial averaging.WannierSTM: Efficient superconducting scanning-probe spectroscopy from maximally localized Wannier functions
We present a Python-based program that combines maximally localized Wannier functions-based first-principles electronic structure with the nonequilibrium Green's function formalism to enable…All-electron machine-learned orbital-free density functional for periodic CHNO systems
We introduce STRUCTURES-P, a machine-learned all-electron orbital-free density functional for periodic CHNO systems.Can Machine Learning Potentials Be More Accurate Than the Methods They Are Trained On?
Although machine learning potentials (MLPs) generalize across configurational and chemical compound spaces, their accuracy is commonly believed to be limited by their training methods, with…Global-in-time Spectral Duhamel-Picard Method for Post-Exposure Bake Simulation and Physics-Informed Neural Operators for EUV Lithography
We present a global-in-time spectral Duhamel-Picard method with polynomial expansions for post-exposure bake (PEB) simulation.Exact Gaussian--Hermite closure of the mapping quantum--classical Liouville equation
In the Meyer--Miller--Stock--Thoss mapping representation, the Liouville operator of the quantum--classical Liouville equation contains an excess coupling term in addition to the Poisson bracket.Thermal aliases in quantum noise: sampling clocks and pulse widths
Can collective quantum-noise measurements distinguish different mode temperatures from one common temperature when the coupling spectrum is unknown?Honeycomb Crystal in a Mass-Asymmetric Electron-Hole Bilayer
A two-dimensional electron gas in a uniform neutralizing background forms a triangular Wigner crystal at low density.Principles and Techniques for Agentic Research in Experimental Physics
The use of artificial intelligence (AI) in physics research is increasingly common, yet few guidelines exist for its effective use.MIRRA: Fast, High-Fidelity Molecular Generation with Chemistry-Informed Discrete Flow Matching
Fast, high-fidelity molecular generation requires accurate chemical choices and efficient sampling.A Method for Exact Enforcement of Boundary Conditions on General Curved Triangular Domains: Algorithm and Application to Scientific Machine Learning
We present a method for enforcing exactly Dirichlet, Neumann, and Robin conditions on general triangular domains with arbitrarily curved boundaries.Structure-preserving fidelity diagnostics for partial differential equations
Machine learning surrogates of partial differential equations (PDEs) are commonly trained on data generated by numerical solvers.FDTD Model of Noise Pollution from an AI Data Center
The sudden growth in AI data centers is raising numerous environmental concerns.Low-Rank Kolmogorov-Arnold Generator Flows for Interpretable Operator Learning
Neural operators that learn physical laws from data should generalize beyond their training distribution and expose the learned law in closed form.Recovering Irreducible Three-Body Interactions with Lagrangian Neural Networks
Lagrangian Neural Networks (LNNs) learn mechanical systems from trajectory data by parameterizing a scalar Lagrangian and deriving the dynamics through the Euler-Lagrange equations, biasing the…
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