Scalable Automated Design and Development of Deep Neural Networks for Scientific and Engineering Applications.

Deep learning is becoming crucial for tackling the increasing modeling complexity of scientific and engineering applications. However, designing high-performing deep neural network (DNN) models can be a challenging and time-consuming task that requires expertise. To address this challenge, we have developed DeepHyper [1], a software package that automates the design and development of DNN models for scientific and engineering applications through scalable neural architecture and hyperparameter search. Our approach emphasizes deep learning over parallel and distributed infrastructures, enabling us to efficiently design and train DNNs for a wide range of scientific applications. In this talk, we will present our recent work on using DeepHyper to automatically generate an ensemble of DNNs at scale and using them to estimate data (aleatoric) and model (epistemic) uncertainties. Our approach enables us to leverage the power of parallel and distributed infrastructures to scale the training of DNNs and improve their performance, while reducing the time and expertise required for manual architecture design and hyperparameter tuning.