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MelissaDL x Breed: Towards Data-Efficient On-Line Supervised Training of Multi-Parametric Surrogates with Active Learning
SessionAI4S: 5th Workshop on Artificial Intelligence and Machine Learning for Scientific Applications
DescriptionArtificial intelligence is transforming scientific computing with deep neural network surrogates that approximate solutions to partial differential equations (PDEs). Traditional off-line training methods face issues with storage and I/O efficiency, as the training dataset has to be computed with numerical solvers up-front. Our previous work, Melissa framework, enables data to be created ``on-the-fly'' and streamed directly into the training process. In this paper, we introduce a new active learning method to enhance data-efficiency of the surrogate training in on-line context. The surrogate is trained to predict a time-step directly with different initial and boundary conditions parameters. Our approach uses Adaptive Multiple Importance Sampling guided by training loss statistics, in order to focus NN training on the difficult areas of the parameter space. Preliminary results for 2D heat PDE demonstrate the potential of this method, called Breed, to improve the generalization capabilities of surrogates while reducing computational overhead.
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