Data-Driven Science and Engineering Seminars
AI Institute in Dynamic Systems
Our mission is to develop the next generation of advanced machine learning tools for controlling complex physical systems by discovering physically interpretable and physics-constrained data-driven models through optimal sensor selection and placement. Our work is anchored by a common task framework that evaluates the performance of machine learning algorithms, architectures, and optimization schemes for the diverse tasks required in engineering applications. We will push beyond the boundaries of modern techniques by closing the loop between data collection, control, and modeling, creating a unique and cross-disciplinary architecture for learning physically interpretable and physics constrained models of complex dynamic systems from time series data.
Extreme Aerodynamics: Flow Analysis and Control for Highly Gusty Conditions
An intrinsic neuronal manifold underlies brain-wide hierarchical organization of behavior in C. elegans
Multifidelity, domain decomposition, and stacking for improving training for physics-informed networks
Advances in Advancing Interfaces: The Mathematics of Manufacturing of Industrial Foams, Fluidic Devices, and Automobile Painting
Learning the shape: streamlining data needs in 2D irregular contour parameterization
Statistical mechanics lessons for data-driven methods
Data Efficient, Robust, and Interpretable Deep Reinforcement Learning for Robotics and Dynamical Systems
