From DIY Neural Operator to NVIDIA PhysicsNeMo
Learn how to leverage NVIDIA PhysicsNeMo for building neural operators, a crucial step in advancing physics-informed neural networks and surrogate modeling, which matters for improving simulation accuracy and efficiency
- Build a DIY neural operator from scratch using Transolver
- Run simulations using the built neural operator to test its accuracy
- Configure NVIDIA PhysicsNeMo for integration with the neural operator
- Test the performance of NVIDIA PhysicsNeMo with the neural operator
- Apply the learned concepts to real-world problems in physics-informed neural networks
Data scientists and AI engineers on a team can benefit from this knowledge to improve their simulation models, while researchers can leverage it to advance the field of physics-informed neural networks
💡 NVIDIA PhysicsNeMo can be used to build and improve neural operators for physics-informed neural networks, leading to more accurate and efficient simulations
⚡️ Leverage NVIDIA PhysicsNeMo for building neural operators and improve simulation accuracy! #AI #Physics
Key Takeaways
Learn how to leverage NVIDIA PhysicsNeMo for building neural operators, a crucial step in advancing physics-informed neural networks and surrogate modeling, which matters for improving simulation accuracy and efficiency
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