NEML2: A High Performance Library for Constitutive Modeling

Technical Report · 01 September 2024

DOI:https://doi.org/10.2172/2440430· OSTI ID:2440430

Hu, Tianchen ^[1]; Messner, Mark C. ^[1]; Schwen, Daniel ^[2]; Munday, Lynn B. ^[2]; Yushu, Dewen ^[2]

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1. Argonne National Laboratory (ANL), Argonne, IL (United States)
2. Idaho National Laboratory (INL), Idaho Falls, ID (United States)

NEML2, the New Engineering Material model Library, version 2, is an offshoot of NEML, an earlier material modeling code developed at Argonne National Laboratory. NEML2 extends the key philosophy of its predecessor, i.e., material models are flexible, modular, and can be built from smaller blocks. It also provides modern features that do not exist in the framework of its predecessor such as material model vectorization, automatic differentiation, device-portable just-in-time compilation, operator fusion, lazy tensor evaluation, etc. Moreover, NEML2 can seamlessly integrate with the popular machine learning package PyTorch to take advantage of modern and fast-growing machine learning techniques. In this fiscal year, the development of core library features and capabilities are complete. The purpose of this report is not to serve as a verbatim copy of the software API reference (which is available online at https://reverendbedford.github.io/neml2/). Instead, this report documents the motivation, implementation, design choices, and usage of each core capability as well as their applications in solving practical engineering problems. This report is compiled based on the NEML2 major release 2.0.0.

• Research Organization: Argonne National Laboratory (ANL), Argonne, IL (United States); Idaho National Laboratory (INL), Idaho Falls, ID (United States)
• Sponsoring Organization: USDOE Office of Nuclear Energy (NE), Nuclear Energy Advanced Modeling and Simulation (NEAMS)
• DOE Contract Number: AC02-06CH11357; AC07-05ID14517
• OSTI ID: 2440430
• Report Number(s): ANL--24/43; 190999
• Country of Publication: United States
• Language: English

Related Subjects

36 MATERIALS SCIENCE
96 KNOWLEDGE MANAGEMENT AND PRESERVATION
97 MATHEMATICS AND COMPUTING