Python tools for solving data-constrained finite element problems
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Updated
Nov 9, 2021 - Python
Python tools for solving data-constrained finite element problems
Evaluate the accuracy, efficiency, and uncertainty-calibration of probabilistic numerical algorithms.
Probabilistic ODE solvers are fun, but are they fast?
IterGP: Computation-Aware Gaussian Process Inference (NeurIPS 2022)
Probabilistic numerical finite differences. Compute finite difference weights and differentiation matrices on scattered data sites and with out-of-the-box uncertainty quantification.
Code for the Paper "Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers"
Probabilistic Numerics in Python.
Probabilistic solvers for differential equations in JAX. Adaptive ODE solvers with calibration, state-space model factorisations, and custom information operators. Compatible with the broader JAX scientific computing ecosystem.
Efficient SDE samplers including Gaussian-based probabilistic solvers. Written in JAX.
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