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Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties
Zitong Zhou, Nicholas Zabaras, Daniel M. Tartakovsky
Identifying the heterogeneous conductivity field and reconstructing the contaminant release history are key aspects of subsurface remediation. Achieving these two goals with limite…
Transformers for Modeling Physical Systems
Nicholas Geneva, Nicholas Zabaras
Transformers are widely used in natural language processing due to their ability to model longer-term dependencies in text. Although these models achieve state-of-the-art performan…
Embedded-physics machine learning for coarse-graining and collective variable discovery without data
Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis
We present a novel learning framework that consistently embeds underlying physics while bypassing a significant drawback of most modern, data-driven coarse-grained approaches in th…