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Tag: physics.comp-ph
arXiv:2604.06287v1 Announce Type: new Abstract: Mathematical models and numerical simulations offer a non-invasive way to explore cardiovascular phenomena, providing access to quantities that cannot be …
arXiv:2604.04971v1 Announce Type: new Abstract: While Physics-Informed Neural Networks offer a promising framework for solving partial differential equations, the standard $L^2$ loss formulation is fundamentally …
arXiv:2604.05303v1 Announce Type: new Abstract: Sampling physical systems with rough energy landscapes is hindered by rare events and metastable trapping. While Boltzmann generators already offer …
arXiv:2604.01712v1 Announce Type: new Abstract: The wind-induced structural response forecasting capabilities of a novel transformer methodology are examined here. The model also provides a digital …
arXiv:2604.01349v1 Announce Type: new Abstract: Reservoir simulation workflows face a fundamental data asymmetry: input parameter fields (geostatistical permeability realizations, porosity distributions) are free to generate …
arXiv:2603.23578v1 Announce Type: new Abstract: Efficient thermal management and precise field prediction are critical for the design of advanced energy systems, including electrohydrodynamic transport, microfluidic …
arXiv:2603.19562v1 Announce Type: new Abstract: Adversarial vulnerability in vision and hallucination in large language models are conventionally viewed as separate problems, each addressed with modality-specific …
arXiv:2603.12556v1 Announce Type: new Abstract: We establish empirical scaling laws for Single-Layer Physics-Informed Neural Networks on canonical nonlinear PDEs. We identify a dual optimization failure: …
arXiv:2603.05538v1 Announce Type: cross Abstract: Data-driven surrogate models improve the efficiency of simulating continuous dynamical systems, yet their autoregressive rollouts are often limited by instability …
arXiv:2603.05598v1 Announce Type: cross Abstract: We investigate the impact of tokeniser pretraining on the accuracy and efficiency of physics emulation. Modern high-resolution simulations produce vast …
arXiv:2603.05560v1 Announce Type: cross Abstract: We investigate the Continuous-Time Koopman Autoencoder (CT-KAE) as a lightweight surrogate model for long-horizon ocean state forecasting in a two-layer …
arXiv:2602.17750v1 Announce Type: cross Abstract: A key problem of solid mechanics is the identification of the constitutive law of a material, that is, the relation …