Domain Aware Deep-learning Algorithms Integrated with Scientific-computing Technologies (DADAIST)

Technical Report · Tue Sep 26 04:00:00 EDT 2023

DOI:https://doi.org/10.2172/2331469· OSTI ID:2331469

Drgona, Jan ^[1]; Tuor, Aaron R. ^[1]; Koch, James V. ^[1]; Shapiro, Madelyn R. ^[1]; King, Ethan ^[1]; Vrabie, Draguna L. ^[1]

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1. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

This technical report summarized the contribution of the DADAIST project funded by the Data Model Convergence Initiative via the Laboratory Directed Research and Development (LDRD) investments at Pacific Northwest National Laboratory (PNNL). Specifically, we report the development of the NeuroMANCER (Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations), a new open-source Scientific Machine Learning library for formulating and solving parametric constrained optimization problems, physics-informed system identification, and parametric optimal control problems. NeuroMANCER is using differentiable programming to combine modern data-driven models and optimization modeling language into a coherent algorithmic and software framework. NeuroMANCER is a Pytorch-based framework and adopts much of its philosophy focused on research and development, rapid prototyping, and streamlined deployment. Strong emphasis is given to extensibility, interoperability with the PyTorch ecosystem, and quick adaptability to custom domain problems. Neuromancer repository contains a comprehensive library of differentiable modules, including custom activation functions, matrix factorizations, deep learning architectures, neural differential equations, differential equation solvers, implicit layers such as iterative solvers, high-level API for symbolic expressions, API for modeling and control of dynamical systems, and extensive set of tutorial code examples in the form of python scripts and jupyter notebooks.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 2331469
• Report Number(s): PNNL--34895
• Country of Publication: United States
• Language: English

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97 MATHEMATICS AND COMPUTING