Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions

Research article (Computer Methods in Applied Mechanics and Engineering, 2019) · cited 111× · AI/ML
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Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions

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Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions is a scholarly article[1].

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APA 4ort.xyz Knowledge Graph. (2026). Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions. Retrieved May 24, 2026, from https://4ort.xyz/entity/machine-learning-materials-physics-integrable-deep-neural-networks-enable-scale-bridging-by-learning-free-energy-functio
MLA “Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/machine-learning-materials-physics-integrable-deep-neural-networks-enable-scale-bridging-by-learning-free-energy-functio.
BibTeX @misc{4ortxyz_machine-learning-materials-physics-integrable-deep-neural-networks-enable-scale-bridging-by-learning-free-energy-functio_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions}}, year = {2026}, url = {https://4ort.xyz/entity/machine-learning-materials-physics-integrable-deep-neural-networks-enable-scale-bridging-by-learning-free-energy-functio}, note = {Accessed: 2026-05-24}}
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