Project Batch 1: Low-Fidelity Physics Informed Machine LearningðŠķ
âĻ Interpretability is the primary focus! âĻ
These projects emphasize interpretability over benchmark performance.
A successful experiment is therefore not defined solely by a low loss value, but by whether the resulting diagnostics support a coherent physical interpretation.
Project Batch Overview
Project 1 asks whether a physics-informed machine learning architecture can be used to solve an inverse problem.
Project 2 builds on this by asking what information remains identifiable in the context of a deliberately imperfect inverse problem.
Project 3 explores what structures can be recovered when the governeing dynamics themselves must be inferred self-consistently from noisy observations?
Project Batch 1 Github Repositories
Thank you for your patience!
Project 3 is still under development and project 1 and 2 artifacts are still being polished.