TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis

Bernoulli and Binomial Random Variables

Random Variables and Probability Distributions

Independence in Probability

Bayes' Theorem Example: Drug Testing 🌿

Bayes' Theorem (with Example!)

The Law of Total Probability

Conditional Probabilities

The Binomial Distribution and the Multinomial Distribution

Quality Control, Non-Destructive Inspection, and the Multinomial Distribution

Mode Sensitivity for Fluid Flows via Lagrangian Coherent Structures

The Birthday Problem in Probability: P(A) = 1 - P(not A)

Set Theory in Probability: Sample Spaces and Events

Counting Probabilities with Combinatorics and the Factorial

Gentle Introduction to Probability: Counting Coin Flips and Dice

Probability and Statistics: Overview

Python Symbolic Regression (PySR) [Physics Informed Machine Learning]

Neural Implicit Flow (NIF) [Physics Informed Machine Learning]

Fourier Neural Operator (FNO) [Physics Informed Machine Learning]

Deep Operator Networks (DeepONet) [Physics Informed Machine Learning]

Hamiltonian Neural Networks (HNN) [Physics Informed Machine Learning]

Lagrangian Neural Network (LNN) [Physics Informed Machine Learning]

Neural Network Control in Collimator 2.0 & New Educational Videos!!!

Residual Networks (ResNet) [Physics Informed Machine Learning]

Neural ODEs (NODEs) [Physics Informed Machine Learning]

Physics Informed Neural Networks (PINNs) [Physics Informed Machine Learning]

SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning

AI/ML+Physics: Preview of Upcoming Modules and Bootcamps [Physics Informed Machine Learning]

AI/ML+Physics: Recap and Summary [Physics Informed Machine Learning]

Using sparse trajectory data to find Lagrangian Coherent Structures (LCS) in fluid flows

AI/ML+Physics Part 5: Employing an Optimization Algorithm [Physics Informed Machine Learning]

The Future of Model Based Engineering: Collimator 2.0

AI/ML+Physics Part 4: Crafting a Loss Function [Physics Informed Machine Learning]

AI/ML+Physics Part 3: Designing an Architecture [Physics Informed Machine Learning]

AI/ML+Physics Part 2: Curating Training Data [Physics Informed Machine Learning]

AI/ML+Physics Part 1: Choosing what to model [Physics Informed Machine Learning]

Physics Informed Machine Learning: High Level Overview of AI and ML in Science and Engineering

Can we make commercial aircraft faster? Mitigating transonic buffet with porous trailing edges

A Neural Network Primer

Supervised & Unsupervised Machine Learning

A Machine Learning Primer: How to Build an ML Model

Arousal as a universal embedding for spatiotemporal brain dynamics

New Advances in Artificial Intelligence and Machine Learning

Nonlinear parametric models of viscoelastic fluid flows with SINDy

[5/8] Control for Societal-Scale Challenges: Road Map 2030 [Technology, Validation, and Transition]

[8/8] Control for Societal-Scale Challenges: Road Map 2030 [Recommendations]

[2/8] Control for Societal-Scale Challenges: Road Map 2030 [Societal Drivers]

[1/8] Control for Societal-Scale Challenges: Road Map 2030 [Introduction]

[6/8] Control for Societal-Scale Challenges: Road Map 2030 [Education]

[3/8] Control for Societal-Scale Challenges: Road Map 2030 [Technological Trends]

[7/8] Control for Societal-Scale Challenges: Road Map 2030 [Ethics, Fairness, & Regulatory Issues]

[4/8] Control for Societal-Scale Challenges: Road Map 2030 [Emerging Methodologies]

🤯🤯🤯 Integrating GPT-4 into Collimator.ai for Symbolic Modeling and Control** 🔥🔥🔥

New Book!!! Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control

Complex Analysis L13: Bromwich Integrals and the Inverse Laplace Transform

Complex Analysis L12: Examples of Complex Integrals

Complex Analysis L11: Examples of Cauchy-Integral Formula

Complex Analysis L10: Cauchy Integral Formula

Complex Analysis L09: Complex Residues

Complex Analysis L08: Integrals in the Complex Plane