MetaPhysiCa: OOD Robustness in Physics-informed Machine Learning

03/06/2023
by   S Chandra Mouli, et al.
2

A fundamental challenge in physics-informed machine learning (PIML) is the design of robust PIML methods for out-of-distribution (OOD) forecasting tasks. These OOD tasks require learning-to-learn from observations of the same (ODE) dynamical system with different unknown ODE parameters, and demand accurate forecasts even under out-of-support initial conditions and out-of-support ODE parameters. In this work we propose a solution for such tasks, which we define as a meta-learning procedure for causal structure discovery (including invariant risk minimization). Using three different OOD tasks, we empirically observe that the proposed approach significantly outperforms existing state-of-the-art PIML and deep learning methods.

READ FULL TEXT

page 1

page 2

page 3

page 4

09/14/2023

Improving physics-informed DeepONets with hard constraints

Current physics-informed (standard or operator) neural networks still re...
01/30/2023

Robust Meta Learning for Image based tasks

A machine learning model that generalizes well should obtain low errors ...
04/18/2023

M-ENIAC: A machine learning recreation of the first successful numerical weather forecasts

In 1950 the first successful numerical weather forecast was obtained by ...
11/12/2020

Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification

Robustness is of central importance in machine learning and has given ri...
02/08/2019

Differentiable Physics-informed Graph Networks

While physics conveys knowledge of nature built from an interplay betwee...
08/05/2014

Machine learning for many-body physics: The case of the Anderson impurity model

Machine learning methods are applied to finding the Green's function of ...

Please sign up or login with your details

Forgot password? Click here to reset