ESPCI web site
Physics-Discovery through Machine Learning for complex fluids and soft matter
The ability to concisely describe the dynamical behavior of soft materials through closed form constitutive relations holds the key to accelerated and informed design of materials and processes. The conventional approach is to construct constitutive relations through simplifying assumptions and approximating the time- and rate-dependent stress response of a complex fluid to an imposed deformation. While traditional frameworks have been foundational to our current understanding of soft materials, they often face a two-fold existential limitation : (i) constructed on ideal and generalized assumptions, precise recovery of material-specific details is usually serendipitous, if possible, and (ii) inherent biases that are involved by making those assumptions commonly come at the cost of new physical insight. This work introduces a novel approach by leveraging recent advances in scientific machine learning methodologies to discover the governing constitutive equation from experimental data for complex fluids. Our Rheology-informed Neural Network (RhiNN) framework is found capable of learning the hidden rheology of a complex fluid through a limited number of experiments. This is followed by construction of an unbiased material-specific constitutive relation that accurately describes a wide range of bulk dynamical behavior of the material. While extremely efficient in closed-form model discovery for a real-world complex system, the model also provides new insight into the underpinning physics of the material.