| Project Detail |
Soft materials play an integral part in many aspects of modern life including autonomy, sustainability, and human health, and their accurate modeling is critical to understand their unique properties and functions. However, the criteria for model selection remain elusive and successful modeling is limited to a few well-trained specialists in the field. My goal is to democratize constitutive modeling through automated model discovery and make it accessible to a more inclusive and diverse community to accelerate scientific innovation. My overall objectives are: i) Establish a new family of constitutive neural networks that simultaneously and fully autonomously discover the model, parameters, and experiment that best explain a wide variety of soft matter systems; ii) Quantify the performance of our discovered models on tension, compression, and shear experiments for the heart, arteries, muscle, lung, liver, skin, brain, hydrogels, silicone, artificial meat, foams, and rubber; and iii) Quantify the uncertainty of our models, parameters, and experiments using a Bayesian analysis. My hypothesis is that automated model discovery will facilitate the exploration of a large parameter space of models and provide unprecedented insights into soft matter systems that are out of reach with conventional theoretical and numerical approaches today. My immediate deliverable is a fully documented open source scientific discovery platform that includes our new neural networks, experimental data, benchmarks, models, and parameters. This discovery platform has the potential to induce a ground-breaking change in constitutive modeling and will forever change how we simulate materials and structures. This project will democratize constitutive modeling; stimulate discovery in soft matter systems; provide deep-learning based tools to characterize, create, and functionalize soft matter; and train the next generation of scientists and engineers to adopt and promote these innovative technologies. |
