DDPS |Scientific Uses of Automatic Differentiation by Michael Brenner

(1:7:56)

DDPS | A flexible and generalizable XAI framework for scientific deep learning

(52:37)

DDPS | Scientific Machine Learning through the Lens of Physics-Informed Neural Networks

(1:2:53)

DDPS | “Infinite Dimensional Optimization for Scientific Machine Learning”

(50:55)

DDPS | Industrial Grade Scientific Machine Learning: Challenges and Opportunities by Santi Adavani

(54:56)

DDPS|Generalizing Scientific Machine Learning and Differentiable Simulation Beyond Continuous models

(57:39)

DDPS | “Multi-fidelity linear regression for scientific machine learning from scarce data”

(1:8)

DDPS | Toward combining principled scientific models and principled machine learning models

(1:5:54)

DDPS | Artificial Intelligence and Scientific Computing for Fluid Mechanics by Petros Koumoutsakos

(1:4:51)

DDPS | “Vertical Reasoning Enhanced Learning, Generation and Scientific Discovery”

(1:9:13)

DDPS | Computational Scientific Discovery: Heuristic Search for Communicable Laws and Models

(1:8:7)

DDPS | Generative Machine Learning Approaches for Data-Driven Modeling and Reductions

(46:4)

DDPS | Scaling Up AI: Embedding Physics Modeling into End-to-end Learning and Harnessing Projection

(1:2:43)

DDPS | Learning to accelerate large-scale physical simulations in fluid and plasma physics

(1:10:7)

DDPS | “A first-principles approach to understanding deep learning”

(1:17:33)

DDPS | ‘Quantum information and data science for modeling classical dynamics’

(1:30:15)

DDPS | “The Weak Form Is Stronger Than You Think”

(1:4:36)

DDPS | “Machine-Precision Neural Networks for Multiscale Dynamics”

(1:8:41)

DDPS | Neural Differentiable Physics

(1:28:24)

DDPS | ML for Solving PDEs: Neural Operators on Function Spaces by Anima Anandkumar

(51:33)