Differential Equations

Quantitative Approximation for Neural Operators in Nonlinear Parabolic Equations ICLR 2025 Safe PDE Boundary Control with Neural Operators L4DC 2025 Sampling in High-Dimensions using Stochastic Interpolants and Forward-Backward Stochastic Differential Equations AISTATS 2025 SINGER: Stochastic Network Graph Evolving Operator for High Dimensional PDEs ICLR 2025 Topological State Space Inference for Dynamical Systems L4DC 2025 Base Models for Parabolic Partial Differential Equations UAI 2024 Iterated INLA for State and Parameter Estimation in Nonlinear Dynamical Systems UAI 2024 Symmetry-Informed Governing Equation Discovery NeurIPS 2024 The Poisson Midpoint Method for Langevin Dynamics: Provably Efficient Discretization for Diffusion Models NeurIPS 2024 A Unified Discretization Framework for Differential Equation Approach with Lyapunov Arguments for Convex Optimization NeurIPS 2023 Competitive Physics Informed Networks ICLR 2023 Counterfactual Dynamics Forecasting – a New Setting of Quantitative Reasoning AAAI 2023 Coupled Multiwavelet Operator Learning for Coupled Differential Equations ICLR 2023 Factorized Fourier Neural Operators ICLR 2023 Self-Supervised Learning with Lie Symmetries for Partial Differential Equations NeurIPS 2023 SYNC: SAFETY-AWARE NEURAL CONTROL FOR STABILIZING STOCHASTIC DELAY-DIFFERENTIAL EQUATIONS ICLR 2023 Mean-field nonparametric estimation of interacting particle systems COLT 2022 Neural graphical modelling in continuous-time: consistency guarantees and algorithms ICLR 2022 Neural Laplace: Learning diverse classes of differential equations in the Laplace domain ICML 2022 Self-Consistency of the Fokker Planck Equation COLT 2022 SPDE-Net: Neural Network based prediction of stabilization parameter for SUPG technique ACML 2021 NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations NeurIPS 2018 Principles of Riemannian Geometry in Neural Networks NeurIPS 2017 Accelerated Mirror Descent in Continuous and Discrete Time NeurIPS 2015 A Differential Equation for Modeling Nesterov’s Accelerated Gradient Method: Theory and Insights NeurIPS 2014