Abstract
A time-honored approach in theoretical materials science revolves around the search for basic mechanisms that should incorporate key feature of the phenomenon under investigation. Recent years have witnessed an explosion across areas of science of a data-driven approach fueled by recent advances in machine learning. Here we provide a brief perspective on the strengths and weaknesses of mechanism based and data-driven approaches in the context of the mechanics of materials. We discuss recent literature on dislocation dynamics, atomistic plasticity in glasses focusing on the empirical discovery of governing equations through artificial intelligence. We conclude highlighting the main open issues and suggesting possible improvements and future trajectories in the fields.
Highlights
The common goal of continuum mechanics is to establish the macroscopic response to a stimulus in a given geometry for a certain material
A well established example is the theory of linear elasticity (Landau and Lifshitz 1986), which can be derived based on general symmetry consideration or even as a large-scale limit of an atomic scale description of a crystal
One can use the same machine learning (ML) algorithms as used for molecular dynamics (MD), discrete dislocation dynamics (DDD) and other particle based simulations or form various density measures with the equation discovery methods we presented in this paper
Summary
The common goal of continuum mechanics is to establish the macroscopic response to a stimulus in a given geometry for a certain material. While the success of ML in speech and image processing is common knowledge new data-driven approaches have outperformed traditional hand-crafted feature methods in computational chemistry as well as linear filtering techniques in computational fluid mechanics.
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