arXiv:2606.09949v1 Announce Type: new Abstract: Data-driven PDE surrogates are trained with data produced by numerical PDE solvers. However, when the surrogate's goal is to generalize across a wide range of PDE configurations (e.g., initial conditions and physical coefficients), generating a representative training set is non-trivial. Uniform sampling of configuration parameters often under-represents trajectories exhibiting challenging dynamics, leading to high prediction errors and large error variance in the trained surrogate. Online training, where data generation and surrogate training ar
This research addresses the fundamental challenge of generating efficient and representative training data for AI models in complex scientific simulations, a critical bottleneck for accelerating scientific discovery.
Improving the efficiency of data generation for PDE surrogates can significantly accelerate research and development in fields relying on complex simulations, from engineering to climate science and drug discovery.
The proposed 'generative active sampling' method offers a more intelligent way to train PDE surrogates, potentially reducing computational costs and improving accuracy for a wide range of scientific and engineering applications.
- · AI researchers (scientific computing)
- · Engineers (design and simulation)
- · Pharmaceutical industry (drug discovery)
- · Climate scientists
- · Traditional numerical solvers (less reliance for quick predictions)
More accurate and faster predictive models for complex physical systems.
Accelerated design cycles for new materials, drugs, and industrial processes due to efficient simulation.
Enhanced AI capabilities for real-time control and optimization of high-stakes physical infrastructure.
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Read at arXiv cs.LG