Lectures related to Sustainable Additive Manufacturing: Advancements and Challenges

Computational Design of Deployable Structures

Explores computational design of deployable structures and programmable mechanical metamaterials with stable memory.

Fracture Mechanics: Crack Growth and Weakest Link

Explores fracture mechanics, crack growth, and the weakest link theory, emphasizing the statistical distribution of crack sizes and the significance of the largest crack in material failure.

Advanced Probabilities: Random Variables & Expected Values

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Variance and Covariance: Properties and Examples

Explores variance, covariance, and practical applications in statistics and probability.

Elements of Statistics: Probability and Random Variables

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

CLIMACT: Advancing Climate Action Initiatives

Presents CLIMACT, a center promoting interdisciplinary research to address climate change challenges and advance societal impact.

Learning Agents: Exploration-Exploitation Tradeoff

Explores the exploration-exploitation tradeoff in learning unknown effects of actions using multi-armed bandits and Q-learning.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Autonomous Vehicles: Trajectory Prediction and Social Behavior

Explores challenges in deep learning for autonomous vehicles, emphasizing social behavior modeling and feasible trajectory prediction.

Trajectory Forecasting in Autonomous Vehicles

Explores trajectory forecasting in autonomous vehicles, focusing on deep learning models for predicting human trajectories in socially-aware transportation scenarios.

Deep Learning Agents: Reinforcement Learning

Explores Deep Learning Agents in Reinforcement Learning, emphasizing neural network approximations and challenges in training multiagent systems.

Neural Networks: Multilayer Learning

Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.

Probability Theory: Random Variables and Distributions

Introduces probability theory, random variables, and distributions, with a focus on their applications in atomic diffusion.

Multilayer Neural Networks: Deep Learning

Covers the fundamentals of multilayer neural networks and deep learning.

Gamma and Neutron Radiography

Explores the historical development and industrial applications of radiography and computerized tomography with X-rays, gammas, and neutrons.

Reconfigurable Metasurfaces: Advanced Optics

Explores the design and applications of reconfigurable metasurfaces in advanced optics, including switchable magnetic metamaterials and responsive metasurfaces.

Model-Based Deep Reinforcement Learning: Monte Carlo Tree Search

Explores model-based deep reinforcement learning, focusing on Monte Carlo Tree Search and its applications in game strategies and decision-making processes.

Self-supervised Learning for Autonomous Vehicles

Explores self-supervised learning for autonomous vehicles, deriving labels from data itself and discussing its applications and challenges.

Glass Microfabrication: Mechanical Structuring

Explores glass microfabrication techniques, including precision machining and ultrasonic machining, focusing on mechanical properties and fracture behavior.

Crack Tip Plasticity in Materials

Explores crack tip plasticity in materials, emphasizing the importance of the plastic zone in crack resistance.