Stochastic Differential Equation Courses and Certifications

Centre International de Rencontres Mathématiques

Numerical Methods for SDEs with Additive Noise and Distributional Drift

Explore numerical methods for stochastic differential equations with distributional drift, covering Euler-type schemes, error analysis, and applications to various noise types.

• YouTube
• 1 hour
• On-Demand
• Free Video

INI Seminar Room 2

A Closed-Form Transition Density Expansion for Elliptic and Hypo-Elliptic SDEs

Discover a groundbreaking closed-form expansion method for analyzing transition densities in elliptic and hypo-elliptic SDEs, with practical applications in parameter inference and stochastic modeling.

• YouTube
• 1 hour 1 minute
• On-Demand
• Free Video

INI Seminar Room 2

Learning Controlled Stochastic Differential Equations

Discover advanced techniques for learning controlled stochastic differential equations in machine learning and statistical prediction uncertainty applications.

• YouTube
• 50 minutes
• On-Demand
• Free Video

Society for Industrial and Applied Mathematics

Deep Solver for Jumps in Backward Stochastic Differential Equations - Lecture

Innovative extension of deep solver for FBSDEs with jumps, using neural networks to tackle high-dimensional problems in option pricing and counterparty credit risk.

• YouTube
• 59 minutes
• On-Demand
• Free Video

Erwin Schrödinger International Institute for Mathematics and Physics (ESI)

Hessian-Aware Stochastic Differential Equation Modeling of SGD

Explore a novel SDE model for SGD that incorporates Hessian information, improving accuracy in capturing escaping behaviors and achieving exact recovery for quadratic objectives.

• YouTube
• 29 minutes
• On-Demand
• Free Video

Advanced statistical physics

We explore statistical physics in both classical and open quantum systems. Additionally, we will cover probabilistic data analysis that is extremely useful in many applications.

• edX
• 10 weeks, 3-5 hours a week
• On-Demand
• Free Online Course (Audit)

Banach Center

Global Approximation of SDEs Driven by Countably Dimensional Wiener Process

Zgłęb globalne przybliżenie równań różniczkowych stochastycznych napędzanych przeliczalnie wymiarowym procesem Wienera.

• YouTube
• 27 minutes
• On-Demand
• Free Video

IISER Pune;  NPTEL

Probabilistic Methods in PDE

Explore probabilistic methods in PDEs, bridging pure and applied mathematics. Learn stochastic calculus, Ito's formula, Feynman-Kac formula, and applications to finance and physics. Ideal for advanced researchers and doctoral students.

• Swayam
• 12 weeks
• On-Demand
• Free Online Course

Generative Memory Lab

Diffusion Models for Inverse Problems

Explore advanced techniques for solving inverse problems using diffusion models, focusing on posterior sampling and manifold constraints.

• YouTube
• 42 minutes
• On-Demand
• Free Video

Hausdorff Center for Mathematics

On Yuansi Chen's Work on the KLS Conjecture I

Explore the KLS conjecture in high-dimensional geometry, its implications for Bourgain's slicing problem, and recent progress using Eldan's Stochastic Localization technique.

• YouTube
• 46 minutes
• On-Demand
• Free Video

Fields Institute

Deeply Learning Derivatives - From Hilbert to Riskfuel

Explore advanced derivatives pricing using deep learning, from Hilbert's 13th problem to modern neural networks for valuing high-dimensional contingent claims in quantitative finance.

• YouTube
• 59 minutes
• On-Demand
• Free Video

MITCBMM

DeepOnet - Learning Nonlinear Operators Based on the Universal Approximation Theorem of Operators

Learn to approximate nonlinear operators using DeepONet, a neural network architecture based on the universal approximation theorem. Explore applications in differential equations, fluid mechanics, and multiphysics problems.

• YouTube
• 58 minutes
• On-Demand
• Free Video

Valence Labs

Action Matching: Learning Stochastic Dynamics from Samples

Explore Action Matching for learning stochastic dynamics from samples. Discover applications in quantum systems, biology, and generative modeling. Gain insights into novel approaches for simulating trajectories.

• YouTube
• 1 hour 52 minutes
• On-Demand
• Free Video

Valence Labs

Stochastic Interpolants: A Unifying Framework for Flows and Diffusions - Lecture

Explore a unified framework for generative models, bridging flow-based and diffusion-based methods using stochastic interpolants. Learn about probability flow equations, likelihood estimation, and applications in AI for drug discovery.

• YouTube
• 1 hour 40 minutes
• On-Demand
• Free Video

Valence Labs

An Optimal Control Perspective on Diffusion-Based Generative Modeling

Explore optimal control theory's connection to diffusion-based generative modeling. Gain insights into stochastic differential equations, Hamilton-Jacobi-Bellman equations, and novel sampling methods for unnormalized densities.

• YouTube
• 1 hour 11 minutes
• On-Demand
• Free Video