The Hypoelliptic Laplacian in Real and Complex Geometry
Explores the hypoelliptic Laplacian's construction and applications in real and complex geometry, focusing on de Rham and Dolbeault theories and its role as a geometric Fokker-Planck operator.
From Mosquitoes to ChatGPT - The Birth and Strange Life of the Random Walk
Explore the fascinating history and diverse applications of random walks, from mosquito control to AI, and their impact on modern technology and scientific understanding.
Optimal Transport and High Dimensional Probability - Introduction to Optimal Transport
Explore optimal transport theory and its applications in high-dimensional probability, focusing on fundamental concepts and recent developments in this mathematical field.
Optimal Transport and High Dimensional Probability in Wasserstein Spaces
Explore optimal transport theory and high-dimensional probability in Wasserstein spaces, delving into advanced mathematical concepts and their applications.
Optimal Transport and High-Dimensional Probability - Functional Inequalities and Concentration of Measure
Explore functional inequalities, concentration of measure, and transport inequalities in high-dimensional probability, including sub-Gaussian variables and the entropy method.
Optimal Transport and High-Dimensional Probability - Langevin Dynamics via Calculus
Explore optimal transport, high-dimensional probability, and Langevin dynamics through advanced calculus techniques in this mathematical deep dive.
Optimal Transport and High-Dimensional Probability - Otto's Calculus on Wasserstein Space
Explore optimal transport and high-dimensional probability through Otto's Calculus on Wasserstein spaces, delving into advanced mathematical concepts and their applications.
Super-H Lesson 5 - More Maths
Explore advanced mathematical operations in Super-H assembly, covering essential commands to enhance programming skills for efficient computation and data manipulation.
College Level Math - Introduction to Polar Equations
Introductory lesson on polar equations for college-level math, covering key concepts to help students succeed in advanced mathematics courses.
Szemerédi Regularity Lemma in Graph Machine Learning
Exploring the Szemerédi regularity lemma's application in graph machine learning, focusing on its theoretical foundations and practical implications for AI-driven graph analysis.
Autoformalization - Ten Years into the Game
Explore past, present, and future approaches to autoformalization in mathematics, examining key developments and potential future directions in this evolving field.
Cueology of Proof
Explore the art and science of constructing mathematical proofs, delving into logical reasoning, problem-solving techniques, and the foundations of formal verification.
Low-Dimensional Approximations of the Conditional Law of Volterra Processes - A Non-Positive Curvature Approach
Explore non-positive curvature approaches for approximating conditional laws of Volterra processes in low dimensions, with applications in machine learning and dynamical systems.
Combinatorial Topological Dynamics
Explore advanced concepts in combinatorial topological dynamics, focusing on machine learning applications and dynamical systems theory in this symposium presentation by Marian Mrozek.
Data-Driven Reduced Order Models of Forced Systems Using Invariant Foliations
Explore innovative techniques for creating data-driven reduced order models of forced systems, focusing on the application of invariant foliations in dynamical systems analysis.
On the Barriers of Robust Koopman Learning
Explore barriers in robust Koopman learning with Matthew Colbrook, examining challenges and advancements in machine learning for dynamical systems.
The Operator is the Model - Dynamical Systems and Machine Learning
Explore the intersection of machine learning and dynamical systems with Igor Mezic, focusing on operator-based modeling approaches and their implications for complex system analysis.
On Finite-Dimensional Approximations of Push-Forwards on Locally Analytic Functionals
Explore finite-dimensional approximations of push-forwards on locally analytic functionals in this advanced mathematical symposium presentation by Isao Ishikawa.
Statistical Learning of Transfer Operators and the Infinitesimal Generator
Explore statistical learning techniques for transfer operators and infinitesimal generators in dynamical systems, focusing on advanced mathematical concepts and applications.
Graphs of Random Matrices in Deep Learning
Explore the intersection of random matrix theory and deep learning, focusing on graph structures and their implications for neural network performance and analysis.
Learning Coarse-Grained Dynamics on Graphs
Explore coarse-grained dynamics on graphs with John Harlim, delving into advanced machine learning techniques for dynamical systems analysis and modeling.