Intro to Deep Learning - ML Tech Talks
Comprehensive introduction to deep learning concepts, neural network architectures, and practical applications, featuring TensorFlow examples, code demos, and insights into representation learning and model interpretability.
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.
Mean Field Limit in Neural Network Learning
Explore the mean field limit in neural network learning, examining optimization dynamics, convergence to global optima, and data representation in autoencoders. Gain insights from physics analogies.
Generating New Molecules Using Machine Learning and AI - Lecture 15
Explore advanced machine learning concepts for molecular generation, from autoencoders and VAEs to GANs, culminating in practical applications for antibiotic discovery using AI.
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.
Intrusive Model Order Reduction Using Neural Network Approximants
Explore innovative neural network-based techniques for efficient reduced-order modeling of complex parametric partial differential equations, focusing on intrusive and interpretable methodologies.