Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences
Fueled by breakthrough technology developments, the biological, biomedical, and
behavioral sciences are now collecting more data than ever before. There is a critical need …
behavioral sciences are now collecting more data than ever before. There is a critical need …
Multiscale modeling meets machine learning: What can we learn?
GCY Peng, M Alber, A Buganza Tepole… - … Methods in Engineering, 2021 - Springer
Abstract Machine learning is increasingly recognized as a promising technology in the
biological, biomedical, and behavioral sciences. There can be no argument that this …
biological, biomedical, and behavioral sciences. There can be no argument that this …
[HTML][HTML] A new family of Constitutive Artificial Neural Networks towards automated model discovery
For more than 100 years, chemical, physical, and material scientists have proposed
competing constitutive models to best characterize the behavior of natural and man-made …
competing constitutive models to best characterize the behavior of natural and man-made …
Physics-informed learning of governing equations from scarce data
Harnessing data to discover the underlying governing laws or equations that describe the
behavior of complex physical systems can significantly advance our modeling, simulation …
behavior of complex physical systems can significantly advance our modeling, simulation …
Weak SINDy for partial differential equations
DA Messenger, DM Bortz - Journal of Computational Physics, 2021 - Elsevier
Abstract Sparse Identification of Nonlinear Dynamics (SINDy) is a method of system
discovery that has been shown to successfully recover governing dynamical systems from …
discovery that has been shown to successfully recover governing dynamical systems from …
Non-invasive inference of thrombus material properties with physics-informed neural networks
We employ physics-informed neural networks (PINNs) to infer properties of biological
materials using synthetic data. In particular, we successfully apply PINNs to extract the …
materials using synthetic data. In particular, we successfully apply PINNs to extract the …
Accelerating phase-field predictions via recurrent neural networks learning the microstructure evolution in latent space
The phase-field method is a popular modeling technique used to describe the dynamics of
microstructures and their physical properties at the mesoscale. However, because in these …
microstructures and their physical properties at the mesoscale. However, because in these …
Variational Onsager Neural Networks (VONNs): A thermodynamics-based variational learning strategy for non-equilibrium PDEs
We propose a thermodynamics-based learning strategy for non-equilibrium evolution
equations based on Onsager's variational principle, which allows us to write such PDEs in …
equations based on Onsager's variational principle, which allows us to write such PDEs in …
Machine learning materials physics: Multi-resolution neural networks learn the free energy and nonlinear elastic response of evolving microstructures
X Zhang, K Garikipati - Computer Methods in Applied Mechanics and …, 2020 - Elsevier
Many important multi-component crystalline solids undergo mechanochemical spinodal
decomposition: a phase transformation in which the compositional redistribution is coupled …
decomposition: a phase transformation in which the compositional redistribution is coupled …
[HTML][HTML] Deep neural network for system of ordinary differential equations: Vectorized algorithm and simulation
TT Dufera - Machine Learning with Applications, 2021 - Elsevier
This paper is aimed at applying deep artificial neural networks for solving system of ordinary
differential equations. We developed a vectorized algorithm and implemented using python …
differential equations. We developed a vectorized algorithm and implemented using python …