{"total":17,"rows":[{"id":"2301.02152","title":"L-HYDRA: Multi-Head Physics-Informed Neural Networks","created":"2023-01-05","cited_by_count":29,"area":null},{"id":"2208.04856","title":"Fully probabilistic deep models for forward and inverse problems in parametric PDEs","created":"2022-08-09","cited_by_count":19,"area":null},{"id":"2301.11040","title":"Random Grid Neural Processes for Parametric Partial Differential Equations","created":"2023-01-26","cited_by_count":8,"area":null},{"id":"2310.04832","title":"HyperSINDy: Deep Generative Modeling of Nonlinear Stochastic Governing Equations","created":"2023-10-07","cited_by_count":8,"area":null},{"id":"2504.19452","title":"Geometry-Informed Neural Operator Transformer","created":"2025-04-28","cited_by_count":8,"area":null},{"id":"2307.12586","title":"InVAErt networks: a data-driven framework for model synthesis and identifiability analysis","created":"2023-07-24","cited_by_count":6,"area":null},{"id":"2307.11289","title":"PI-VEGAN: Physics Informed Variational Embedding Generative Adversarial Networks for Stochastic Differential Equations","created":"2023-07-21","cited_by_count":4,"area":null},{"id":"2409.06560","title":"A Primer on Variational Inference for Physics-Informed Deep Generative Modelling","created":"2024-09-10","cited_by_count":3,"area":null},{"id":"2506.13658","title":"Adversarial Disentanglement by Backpropagation with Physics-Informed Variational Autoencoder","created":"2025-06-16","cited_by_count":2,"area":null},{"id":"2509.08515","title":"Variational Rank Reduction Autoencoders for Generative Thermal Design","created":"2025-09-10","cited_by_count":1,"area":null},{"id":"2302.03689","title":"PartitionVAE -- a human-interpretable VAE","created":"2023-02-04","cited_by_count":0,"area":null},{"id":"2308.12312","title":"Physics informed Neural Networks applied to the description of wave-particle resonance in kinetic simulations of fusion plasmas","created":"2023-08-23","cited_by_count":0,"area":null},{"id":"2503.18012","title":"Scalable physics-informed deep generative model for solving forward and inverse stochastic differential equations","created":"2025-03-23","cited_by_count":0,"area":null},{"id":"2601.08104","title":"High-Fidelity Modeling of Stochastic Chemical Dynamics on Complex Manifolds: A Multi-Scale SIREN-PINN Framework for the Curvature-Perturbed Ginzburg-Landau Equation","created":"2026-01-13","cited_by_count":0,"area":null},{"id":"2606.22775","title":"Target-Aware Linear Regression Under Distribution Shift","created":"2026-06-22","cited_by_count":0,"area":null},{"id":"2608.12624","title":"Structure-preserving uncertainty quantification for GENERIC dynamics","created":"2026-08-12","cited_by_count":0,"area":null},{"id":"2610.04405","title":"CEENs: Causality-enforced evolutional networks for solving time-dependent partial differential equations","created":"2026-10-03","cited_by_count":0,"area":null}],"next_cursor":null}