Probabilistic Methods for Data-Driven Reduced-Order Modeling
Explore probabilistic methods for data-driven reduced-order modeling, including Gaussian process regression, deep kernel learning, and Bayesian reduced-order operator inference for efficient multi-query simulations and digital twinning.
AutoEncoders for Aerodynamic Predictions
Explore autoencoders for aerodynamic predictions, focusing on flow past wing geometries. Learn about non-linear compression, latent representations, and uncertainty quantification in data-driven modeling.
Neural Differentiable Physics: Unifying Numerical PDEs and Deep Learning for Data-Augmented Computational Physics
Explore Neural Differentiable Physics, unifying numerical PDEs and deep learning for enhanced computational modeling of complex physical systems. Learn innovative SciML approaches integrating physics principles with AI.
Probabilistic Programming Tutorial - Part 1
Explore probabilistic programming fundamentals and applications with MIT expert Vikash Mansinghka in this comprehensive tutorial.
Robust Optimization and Generalization
Explore robust optimization's history and its modern machine learning applications, focusing on performance certification and practical challenges in ML systems.
Nematic Liquid Crystals: Where Mathematics Meets Physics and Applications
Explore the fascinating intersection of mathematics, physics, and applications in nematic liquid crystals with Prof. Apala Majumdar's insightful lecture.
Generative Sequence Models for Sequential Decision Making
Explore generative sequence models for decision-making under uncertainty. Learn how Transformer architecture and language modeling advances can enhance offline learning, online exploration, and multi-task generalization in AI.
Probabilistic Modeling and Programming Tutorial - Lecture 1
Explore probabilistic modeling and programming techniques with expert guidance, enhancing your AI and machine learning skills through hands-on tutorials and practical examples.
Geometric Probabilistic Models - Tutorial 4
Explore geometric probabilistic models for data-efficient, uncertainty-aware decision-making in applications like drug design and robotics. Learn theory and implementation of Gaussian processes and related techniques.
Gaussian Processes
Explore Gaussian Processes with Daniel Hernández-Lobato, covering key concepts, applications, and advanced techniques in probabilistic machine learning.