arXiv:2510.17569v3 Announce Type: replace
Abstract: Generative deep learning techniques have demonstrated an impressive capacity for tackling biomolecular design problems in recent years. Despite their high performance, however, they still suffer from…
arXiv:2604.03197v1 Announce Type: new
Abstract: Cardiovascular modeling has rapidly advanced over the past few decades due to the rising needs for health tracking and early detection of cardiovascular diseases. While 1-D arterial models offer an attra…
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 in arbitrary quantities, y…
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 twin component for bridge structural health monitorin…
arXiv:2604.01231v1 Announce Type: cross
Abstract: For most process systems, knowledge of the model structure is incomplete. This missing physics must then be learned from experimental data. Recently, a combination of universal differential equations a…
arXiv:2604.02121v1 Announce Type: cross
Abstract: Stochastic kinetic models are ubiquitous in physics, yet inferring their parameters from experimental data remains challenging. In deterministic models, parameter inference often relies on gradients, a…
arXiv:2603.21291v2 Announce Type: replace
Abstract: We propose closed-form conditional diffusion models for data assimilation. Diffusion models use data to learn the score function (defined as the gradient of the log-probability density of a data dist…
arXiv:2512.18147v2 Announce Type: replace-cross
Abstract: Accurate calculations of solvation free energies remain a central challenge in molecular simulations, often requiring extensive sampling and numerous alchemical intermediates to ensure sufficie…
arXiv:2604.00333v1 Announce Type: cross
Abstract: Collective behaviors that emerge from interactions are fundamental to numerous biological systems. To learn such interacting forces from observations, we introduce a measure-valued neural network that …
arXiv:2604.00675v1 Announce Type: cross
Abstract: Mechanistic simulations typically assume fixed ontologies: variables, causal relationships, and resolution policies are static. This assumption fails when the true causal structure is contested or unid…