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With rapid progress in deep learning, neural networks have been widely usedin scientific research and engineering applications as surrogate models.Despite the great success of neural networks in fitting complex systems, twomajor challenges still remain: i) the lack of generalization on differentproblems/datasets, and ii) the demand for large amounts of simulation data thatare computationally expensive. To resolve these challenges, we propose thedifferentiable \mf (DMF) model, which leverages neural architecture search(NAS) to automatically search the suitable model architecture for differentproblems, and transfer learning to transfer the learned knowledge fromlow-fidelity (fast but inaccurate) data to high-fidelity (slow but accurate)model. Novel and latest machine learning techniques such as hyperparameterssearch and alternate learning are used to improve the efficiency and robustnessof DMF. As a result, DMF can efficiently learn the physics simulations withonly a few high-fidelity training samples, and outperform the state-of-the-artmethods with a significant margin (with up to 58$\%$ improvement in RMSE) basedon a variety of synthetic and practical benchmark problems.

Differentiable Multi-Fidelity Fusion: Efficient Learning of Physics Simulations with Neural Architecture Search and Transfer Learning