SciML: Novel Scientific Discoveries through composability

SciML provides tools for a wide problem space. It can be confusing for new users to decide between the packages and the kind of questions that can be answered using each of them. This talk will walk through various ecosystem components for tasks such as inverse problems, model augmentation, and equation discovery and showcase workflows for using these packages with examples based on real-world data.

SciML provides tooling for various Scientific Machine Learning tasks, including parameter estimation, model augmentation, equation discovery, ML-based solvers for differential equations, and surrogatization. It can be confusing for new users to reason about the various packages, including DiffEqParamEstim, DiffEqFlux, DataDrivenDiffEq, NeuralPDE, and Surrogates etc., and their suitability for the problem they want to solve. We plan to provide a wide overview of the SciML ecosystem packages, describing the kinds of questions that each of these packages is suitable to answer. Additionally, we will demonstrate sample SciML workflows that show the composability of the ecosystem.