Paris Perdikaris, University of Pennsylvania
Luca Dede, Politecnico di Milano
Biology, biomedicine, and behavioral sciences are currently witnessing a shift from solving forward problems based on sparse data towards data assimilation and solving inverse problems to explain large datasets. Very often, multi-scale simulations in biomedicine and bioengineering seek to infer the behavior of a system, assuming access to massive amounts of data, while the governing equations and their parameters may be uncertain. This is where machine learning may become critical: machine learning allows us to systematically preprocess massive amounts of data, integrate and analyze it from different input modalities and different levels of fidelity, identify correlations, discover hidden physics and infer the dynamics of the overall system, as well as build reduced order models for forward and inverse uncertainty quantification tasks. Similarly, we can use machine learning to tackle high-dimensional forward-predictive tasks, as well as inverse problems that aim to calibrate large-scale computational models to reproduce experimentally measured features across multiple scales. This mini-symposium aims to showcase current advances in machine learning and uncertainty quantification methods for biological systems, including (but not limited to) topics in data fusion, experimental design, Bayesian inference, systems identification, multi-fidelity modeling, hybrid model- and data-driven approaches, parameter estimation, data assimilation, and model personalization in computational medicine.