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 7
Encoding Matters: A Qubit-Efficient Reformulation for Quantum Resilient Routing
Qubits are powerful computational resources because each additional qubit doubles the size of the accessible state space.Learning qBIC Resonances across Metasurface Families in Dielectric Fourier Space
Bound states in the continuum (BIC) metasurfaces are typically described by geometry-specific parameters, hindering cross-geometry comparison, while ultranarrow qBIC features are easily diluted in…Ghost tasking for parametrized Gaussian Processes solving linear differential equations
Physics-informed machine learning has gained significant attention in recent years.Prior or Feedback? What an LLM Uses When Adapting Neural Operators
Do LLM scientific agents rely only on their initial task context, or do they adapt their decisions in response to experimental feedback?Bi-FORK: Generative Modeling of High-Dimensional Bifurcating Systems
Bifurcations are ubiquitous in physical systems, from structural buckling to fluid and climate dynamics, yet they remain largely unexplored in deep learning.Polyconvex neural-network surrogates with learned paired directional features for anisotropic hyperelastic homogenization
Approximating the effective anisotropic hyperelastic response of a representative volume element (RVE) requires constitutive expressiveness, mechanical consistency, and computational efficiency.Origins of Universal Machine Learning Force-Field Errors in Multicomponent Materials
Universal machine learning force-field generalization to multicomponent environments generated by compositional design remains insufficiently assessed.LLM-Assisted Generation of Transparent, Open-Source Multiphysics Models of Electrochemical Devices
Multiphysics continuum models are powerful tools for studying electrochemical devices, enabling in silico reactor design and resolution of local pH, potential, and concentration fields that govern…NEMORA: Neural Equivariant Multipole Operators for Long-Range Atomistic Learning
Equivariant graph neural networks have emerged as foundational architectures for machine-learned interatomic potentials, approaching quantum-chemical accuracy at a fraction of the computational cost.Gen-PINNs: Generative Adversarial Physics Informed Neural Networks for solving partial differential equations
Physics-Informed Neural Networks (PINNs) are a widely used data-free method for solving Partial Differential Equations (PDEs) using machine learning.Explore, Then Commit: Measurement-Efficient Scientific Law Discovery with Language Models
Scientific law discovery requires selecting measurements and converting evidence into a governing equation.Demonstration of Parallel Multi-QPU Execution for Fragment-Based Quantum Chemistry Using On-Premises Hardware
Fragment molecular orbital (FMO) quantum chemistry offers a natural route to parallel quantum computing: large molecular calculations can be decomposed into smaller quantum subproblems that can, in…Learning consistent molecular mechanics force fields from first principles
Classical force fields (FFs) remain the workhorse for large-scale simulations even as machine-learned interatomic potentials (MLIPs) approach ab initio accuracy.Atom-JEPA: Joint-Embedding Predictive Architecture for 3D Atomistic Systems
Large-scale self-supervised pretraining has reshaped modern machine learning, substantially advancing the ability of language and vision models to generalize across downstream tasks.Early onset of the hot circumgalactic medium around Milky Way galaxies
Hot coronae are key regulators in Milky Way (MW)-type galaxy formation and are thought to emerge once a galaxy's dark matter halo crosses a critical mass threshold, $M_{\mathrm{vir}}\sim10^{12}…Convergence of Roberts flow dynamos in Eulerian and Lagrangian codes
Standard benchmark tests for numerical magnetohydrodynamics codes focus on one- and two-dimensional problems that ignore the possibility of dynamos, i.e., an exponential instability converting…Quantum data loading from the learned shared structure of real signals
Preparing quantum states from classical data can cost more than the computation they serve; most loaders tailor a circuit to each input.GPU-Initiated Discrete Simulated Bifurcation: Low-Latency Requests and Streaming Dense Couplings
GPU-based optimization faces two communication bottlenecks: coordinating frequent requests and delivering dense models that exceed device memory.Molecular hydrogen formation on dust: The impact of gas-dust drift on formation efficiency
Molecular hydrogen is predominantly formed on dust-grain surfaces in the interstellar medium, where relative gas-dust motion can arise in dynamically active environments.Physics-Augmented Graph Transformers for Patch-Antenna Forward and Inverse Design
Full-wave electromagnetic (EM) simulation enables accurate patch-antenna analysis but is computationally expensive for large-scale forward prediction and inverse design.
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