HIGHLIGHTS
SUMMARY
The capacity to exploit the vast ecosystem of scientific programming tools that have been developed for Python, particularly those in machine_learning and data science. Related to the second point, the recent machine_learning revolution has led to the developing area of hybrid machine-learning-computational-mechanics procedures, including numerous research avenues: machine_learning-acceleration of solvers; machine_learning-constitutive laws in solid mechanics; machine_learning-turbulence models in fluid dynamics; and targeting both acceleration and accuracy gains. In these approaches, the first challenge involves the development of a machine_learning procedure - typically based on a neural_network - capable of solving . . .
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