ABSTRACT

Scientific discovery--the process of gaining new knowledge about the natural world from empirical observations of complex physical processes--has long been a topic of research. While historical approaches often necessitated the lifelong collection of data and analysis of talented scholars, modern computing technologies have revolutionized methods of scientific discovery. Today, advanced computing machines and machine learning (ML) facilitate the rapid analysis of large-scale complex data. However, current approaches often rely on opaque "black-box" ML models. They suffer from a lack of physically-consistency, generalizability, and/or interpretability. To address these limitations, this CAREER project focuses on establishing a synergistic research and education program to advance methods of scientific discovery. The core research idea of the project is to embed physics domain knowledge into the design of deep-learning (DL) models. By augmenting strong physics-based model designs with carefully formulated hypotheses and massive data collections, this approach will produce novel DL models that enforce the principles of physics to strongly restrict search spaces of model outcomes to be physically consistent. This innovative approach is expected to greatly accelerate the process of scientific discovery and enhance its efficiency, accuracy, and interpretability. The expected outcome of this project is also widely applicable to relevant research areas such as Thermodynamics and Molecular dynamics.

This project investigates data-driven scientific discovery of complex spatiotemporal physical processes. It aims to achieve the following specific goals: to develop (1) physically-consistent dynamics models, which exactly preserve important physical characteristics by imposing strong physics biases to the design of neural networks; (2) DL-based symbolic regression algorithms to infer interpretable mathematical expressions of target dynamics models, where the model structures are designed to conform to principles of physics; and, (3) multimodal DL algorithms for scientific applications that leverage data that exist in different modalities. In addition, this project seeks to develop (4) physics-inspired DL models for data science downstream tasks by leveraging findings from the above tasks, contributing to ML research. An interdisciplinary course will be developed based on the project outcomes to educate researchers, students at all levels, and the broader communities. Lastly, this project offers research opportunities for students, prioritizing recruitment from underrepresented groups.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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