Showing 1–20 of 23 results
/ Date/ Name
Jun 25, 2024Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient FlowsJan 24, 2025Point Cloud Neural Operator for Parametric PDEs on Complex and Variable GeometriesFeb 2, 2026Geometric Generalization of Neural Operators from Kernel Integral PerspectiveFeb 21, 2023Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine InvarianceJul 22, 2024Fisher-Rao Gradient Flow: Geodesic Convexity and Functional InequalitiesApr 9, 2022Efficient Derivative-free Bayesian Inference for Large-Scale Inverse ProblemsMar 24, 2022The Cost-Accuracy Trade-Off In Operator Learning With Neural NetworksJan 8, 2025Stable Derivative Free Gaussian Mixture Variational Inference for Bayesian Inverse ProblemsSep 8, 2024Online learning of eddy-viscosity and backscattering closures for geophysical turbulence using ensemble Kalman inversionJun 16, 2025Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic ModelsJan 21, 2026Adaptive Exponential Integration for Stable Gaussian Mixture Black-Box Variational InferenceOct 19, 2021Long Random Matrices and Tensor UnfoldingFeb 2, 2021Iterated Kalman Methodology For Inverse ProblemsApr 15, 2024Convergence Analysis of Probability Flow ODE for Score-based Generative ModelsDec 12, 2023SPFNO: Spectral operator learning for PDEs with Dirichlet and Neumann boundary conditionsApr 27, 2023AI-aided Geometric Design of Anti-infection CathetersFeb 8, 2024An operator learning perspective on parameter-to-observable mapsSep 19, 2025Improving Monte Carlo Tree Search for Symbolic RegressionMay 20, 2021Bayesian Calibration for Large-Scale Fluid Structure Interaction Problems Under Embedded/Immersed Boundary FrameworkJul 11, 2022Fourier Neural Operator with Learned Deformations for PDEs on General Geometries