Главная
Study mode:
on
You
History
Saved
In progress 0 courses
compleat 0 courses
#Art & Design #Adobe #ChatGPT #GitHub

#Physics Informed Machine Learning

Showing: 21 courses
Steve Brunton

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

0 rewiews
Explore how to integrate physics into machine learning, enhancing model accuracy and efficiency in engineering applications. Learn to leverage prior physical knowledge across all stages of the ML process.
Add to list
12 Lesons
47 minutes
On-Demand
Free-Video
ADC - Audio Developer Conference

Pipeline for VA Modelling with Physics-Informed Machine Learning

0 rewiews
Explore a VA modelling pipeline using Differentiable Wave Digital Filters and neural networks for accurate, real-time circuit modelling, demonstrated with a diode clipper circuit.
Add to list
1 Lesons
32 minutes
On-Demand
Free-Video
Alan Turing Institute

Uncertainty Quantification with Physics-Informed Machine Learning

0 rewiews
Exploring uncertainty quantification in physics-informed machine learning, focusing on architecture and learning approaches for scientific applications and complex real-world phenomena.
Add to list
1 Lesons
1 hour 3 minutes
On-Demand
Free-Video
MICDE University of Michigan

UQ for ML and ML for UQ - Uncertainty Quantification and Machine Learning in Physics-Based Modeling

0 rewiews
Explore the interplay between Uncertainty Quantification and Machine Learning in physics-based modeling, examining their roles in solving complex computational challenges across various applications.
Add to list
1 Lesons
57 minutes
On-Demand
Free-Video
Inside Livermore Lab

Data-Driven Closure Modeling Using Derivative-free Kalman Methods

0 rewiews
Explore data-driven closure modeling for complex dynamical systems using derivative-free Kalman methods. Learn to improve predictions in turbulence and cloud dynamics with advanced machine learning techniques.
Add to list
1 Lesons
1 hour 4 minutes
On-Demand
Free-Video
Steve Brunton

Deep Learning to Discover Coordinates for Dynamics - Autoencoders & Physics Informed Machine Learning

0 rewiews
Explore deep learning techniques for discovering effective coordinate systems in dynamical systems, enabling simpler models and physical law discovery. Focus on autoencoders and physics-informed machine learning.
Add to list
15 Lesons
27 minutes
On-Demand
Free-Video
Alan Turing Institute

Machine Learning in Fluid Dynamics and Climate Physics

0 rewiews
Explore machine learning applications in fluid dynamics and climate modeling, including physics-informed approaches, data integration, and simulation validation techniques.
Add to list
1 Lesons
1 hour 2 minutes
On-Demand
Free-Video
Alan Turing Institute

Unique Challenges in Physics-Informed Machine Learning

0 rewiews
Explores unique challenges in physics-informed machine learning, focusing on non-convexity in loss functions and insufficient model expressibility compared to conventional machine learning approaches.
Add to list
1 Lesons
55 minutes
On-Demand
Free-Video
Steve Brunton

Neural ODEs - From ResNet to Continuous-Time Machine Learning

0 rewiews
Explore Neural ODEs: a powerful machine learning approach for learning ODEs from data. Discover its advantages over ResNet and extensions like HNNs and LNNs.
Add to list
11 Lesons
25 minutes
On-Demand
Free-Video
Steve Brunton

Designing an Architecture for Physics-Informed Machine Learning - Part 3

0 rewiews
Explore various neural network architectures for physics-informed machine learning, including ResNets, UNets, and PINNs. Learn how to incorporate physical principles and symmetries into model design for improved performance.
Add to list
15 Lesons
37 minutes
On-Demand
Free-Video
Alan Turing Institute

Feature Encoded and Multi-Resolution Physics-Informed Machine Learning for Musculoskeletal Digital Twins

0 rewiews
Innovative machine learning approaches for musculoskeletal digital twin applications, focusing on parameter identification and motion prediction using surface electromyography signals.
Add to list
1 Lesons
47 minutes
On-Demand
Free-Video
Steve Brunton

Crafting a Physics-Informed Loss Function for AI/ML in Physics - Part 4

0 rewiews
Explores designing physics-informed loss functions in AI/ML, including case studies on fluid dynamics, sparse modeling, and equivariant loss, to enhance model performance and incorporate physical constraints.
Add to list
10 Lesons
23 minutes
On-Demand
Free-Video
Steve Brunton

Neural Implicit Flow - Physics Informed Machine Learning

0 rewiews
Explore physics-informed machine learning with Neural Implicit Flow, covering turbulent data compression, sparse sensor placement, and mesh-agnostic approaches for advanced fluid dynamics applications.
Add to list
10 Lesons
14 minutes
On-Demand
Free-Video
Steve Brunton

Python Symbolic Regression - Physics Informed Machine Learning

0 rewiews
Explore Python Symbolic Regression for physics-informed machine learning. Learn algorithm basics, historical context, and PySR tool benefits for discovering interpretable mathematical models from data.
Add to list
13 Lesons
16 minutes
On-Demand
Free-Video
Inside Livermore Lab

Elliptic PDE Learning: Provably Data-Efficient Techniques

0 rewiews
Explore data-efficient PDE learning techniques, combining deep learning with physics and mathematics to discover properties of unknown physical systems from limited experimental data.
Add to list
1 Lesons
59 minutes
On-Demand
Free-Video
GERAD Research Center

Physics-Informed Machine Learning for Complex Systems - A Focus on Power System Applications

0 rewiews
Explorez l'application de l'apprentissage automatique aux systèmes électriques, en mettant l'accent sur les modèles réduits et la calibration à partir de données enregistrées pour améliorer la résilience du réseau.
Add to list
1 Lesons
52 minutes
On-Demand
Free-Video
RWTH Center for Artificial Intelligence

Probabilistic Simulation Methods for Machine Learning in Science and Technology

0 rewiews
Explore probabilistic numerics in machine learning, bridging computation and inference. Gain insights into physics-informed ML and adaptive solver integration for enhanced efficiency in scientific computing.
Add to list
1 Lesons
45 minutes
On-Demand
Free-Video
Alan Turing Institute

A Connection between Probability, Physics and Neural Networks

0 rewiews
Explore the intersection of probability, physics, and neural networks, focusing on constructing physics-consistent models using Gaussian processes and the central limit theorem.
Add to list
1 Lesons
1 hour 11 minutes
On-Demand
Free-Video
Inside Livermore Lab

Physics-Enhanced Gaussian Processes for Learning of Electromechanical Systems

0 rewiews
Explore physics-enhanced Gaussian processes for modeling electromechanical systems. Learn about GP-PHS and physics-enhanced variational autoencoders to improve data efficiency and ensure physical correctness.
Add to list
1 Lesons
1 hour 15 minutes
On-Demand
Free-Video
Inside Livermore Lab

Physics Informed Machine Learning through Symbolic Regression

0 rewiews
Explore symbolic regression for identifying PDE models from scarce, noisy data. Compare with Physics-Informed Neural Networks and discuss applications in fault detection and system health prognostics.
Add to list
1 Lesons
1 hour 9 minutes
On-Demand
Free-Video
MICDE University of Michigan

Aurora: A Foundation Model of the Atmosphere - Tutorial 1

0 rewiews
Explore Aurora, a groundbreaking foundation model for atmospheric science, and learn its applications in climate modeling and weather prediction.
Add to list
1 Lesons
2 hours 4 minutes
On-Demand
Free-Video