Model-Constrained Deep Learning Approaches for Scientific Machine Learning
Explore model-aware machine learning methods combining scientific computing and data-driven models. Learn about ROM-ML and Autoencoder-based Inversion approaches for PDE-constrained inverse problems.
Differentiable Programming for Modeling and Control of Dynamical Systems
Explore differentiable programming for optimal control of dynamical systems, focusing on differentiable predictive control and its applications in building control and power systems optimization.
Scientific Machine Learning through the Lens of Physics-Informed Neural Networks
Explore physics-informed neural networks (PINNs) as an alternative to traditional scientific computation, addressing limitations and showcasing applications in solving complex physical problems.
Deep Neural Operators with Reliable Extrapolation for Multiphysics and Multiscale Problems
Explore deep neural operators for complex systems, focusing on DeepONet and its extensions for multiphysics and multiscale problems. Learn about extrapolation challenges and solutions in deep learning models.
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.
Towards Data-Driven High Fidelity CFD - Lecture 2
Explore data-driven approaches for high-fidelity computational fluid dynamics in this advanced lecture on scientific machine learning and CFD techniques.
Towards Data-Driven High Fidelity CFD - Lecture 1
Explore data-driven high fidelity CFD, covering shock droplet interaction, icing, multiscale analysis, DG schemes, and scientific machine learning applications in computational fluid dynamics.
Data-Driven Latent Representations for Time-Dependent Problems - Lecture 3
Explore data-driven latent representations for time-dependent problems, focusing on applications in climate downscaling, superresolution, and optimal transport.
Linear and Nonlinear Schemes for Forward Model Reduction and Inverse Problems - Lecture 3
Explore linear and nonlinear schemes for forward model reduction and inverse problems in this advanced mathematics lecture by Olga Mula.
Finite Neuron Method - Lecture 1
Explore finite neuron method fundamentals, covering finite element method, neural networks, and adaptive PD. Gain insights into mathematical concepts and their applications.
Neural Scaling Laws - Fitting Scaling Laws for Scientific Foundation Models - Tutorial 3
Explore neural scaling laws and learn techniques for fitting scaling laws to scientific foundation models in this advanced tutorial.