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Found 9 papers, 6 papers with code
Joint Parameter and Parameterization Inference with Uncertainty Quantification through Differentiable Programming
Accurate representations of unknown and sub-grid physical processes through parameterizations (or closure) in numerical simulations with quantified uncertainty are critical for resolving the coarse-grained partial differential equations that govern many problems ranging from weather and climate prediction to turbulence simulations.
Multi-fidelity climate model parameterization for better generalization and extrapolation
To extrapolate beyond the training data, the MF-RPNs are tested on high-fidelity warming scenarios, $+4K$, data.
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators.
Scalable Bayesian optimization with high-dimensional outputs using randomized prior networks
Several fundamental problems in science and engineering consist of global optimization tasks involving unknown high-dimensional (black-box) functions that map a set of controllable variables to the outcomes of an expensive experiment.
History-Based, Bayesian, Closure for Stochastic Parameterization: Application to Lorenz '96
To address these issues, we develop a new type of parameterization (closure) which is based on a Bayesian formalism for neural networks, to account for uncertainty quantification, and includes memory, to account for the non-instantaneous response of the closure.
Fast PDE-constrained optimization via self-supervised operator learning
Design and optimal control problems are among the fundamental, ubiquitous tasks we face in science and engineering.
Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data
This paper presents a machine learning framework (GP-NODE) for Bayesian systems identification from partial, noisy and irregular observations of nonlinear dynamical systems.
Model-Order-Reduction Approach for Structural Health Monitoring of Large Deployed Structures with Localized Operational Excitations
Such problem has $45$ parameters and shows the merits of the two-level PR-RBC approach and of the correlation function-based features in the context of operational excitations, other nuisance parameters and added noise.
Numerical Analysis Numerical Analysis
Bayesian differential programming for robust systems identification under uncertainty
This paper presents a machine learning framework for Bayesian systems identification from noisy, sparse and irregular observations of nonlinear dynamical systems.
