This workshop explores the intersection of uncertainty quantification (UQ) and machine learning (ML) in modeling and analyzing intricate physical phenomena. Participants will examine the challenges of quantifying uncertainties in complex systems across various scientific and engineering domains. The workshop will cover advanced UQ techniques, including Bayesian inference, sensitivity analysis, and probabilistic modeling, tailored for complex physical systems. Attendees will delve into cutting-edge machine learning approaches, such as physics-informed neural networks, deep learning for differential equations, and transfer learning, applied to physical system modeling. The workshop will emphasize the synergy between UQ and ML, exploring how these fields can complement each other to enhance prediction accuracy and reliability in complex systems. Through interactive lectures and group discussions, participants will gain insights into implementing these methods in their research or industrial applications. This workshop is designed for researchers, engineers, and data scientists working with complex physical systems in fields such as fluid dynamics, climate modeling, aerospace engineering, and beyond. Attendees will leave equipped with state-of-the-art knowledge to tackle uncertainty and complexity in their respective domains.
Funding
Priority funding consideration will be given to those to register by March 18, 2025. Funding is limited.
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