Day 2 – Optional Hands-On Coding

Costs $25 extra with the Symposium registration or $50 as a stand-alone

Offered by our invited speaker Jan Drgona from Pacific Northwest National Lab (US).

Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations (NeuroMANCER) is an open-source differentiable programming (DP) library for solving parametric constrained optimization problems, physics-informed system identification, and parametric model-based optimal control. NeuroMANCER is written in PyTorch and allows for systematic integration of machine learning with scientific computing for creating end-to-end differentiable models and algorithms embedded with prior knowledge and physics.