• Pietro Carrara, Eth Zurich
• Francisco Chinesta
• Laura De Lorenzis
• Siddhant Kumar
• Pierre Ladeveze
• Michael Ortiz
• Stefanie Reese

Data-driven and machine learning techniques are attracting a steadily increasing interest in computational solid mechanics. Recent studies highlight that data-driven approaches may extend and encompass classical approaches and suggest a vast unexplored potential for their applications in computational solid mechanics. As a result, the research in this field has now blossomed into several state-of-the-art directions - from deep neural networks for highly non-linear and high-dimensional surrogate models, to experimental-data-based model-free approaches that aim at eliminating modeling bias, and discovery of interpretable constitutive models and governing equations as opposed to black-box techniques. More recently, a trend is emerging towards reducing data-dependency by integrating physics-based knowledge and modeling with data-driven procedures. The most prominent applications include acceleration of multiscale simulations, material characterization, computational design and optimization of (meta-)materials.
This mini-symposium aims at discussing advances in data-driven and machine learning approaches in solid mechanics and in their applications, with representative topics that include but are not limited to
・Model-free data-driven computational mechanics
・Supervised/Unsupervised learning of surrogate models
・Data-driven discovery of constitutive laws and governing equations
・Data-driven identification procedures
・Integration of physics- and data-driven methods
・Application to acceleration of multiscale simulations
・Application to characterization and design of materials and structures