Physics-informed Machine Learning for Control – DNN in a Dynamic Feedback Loop

This Case Study describes an approach to combining physical principles with Machine Learning (ML) for modeling and control of complex systems. Our approach was developed as part of a DARPA-funded research project. It was applied to oil reservoir management. While this Case Study provides an overview, technical details may be found in a separate publication [1].

BIM to FEM Services

BIM to FEM Services Complex BIM Geometry Directly into Advanced FEM BIM to FEM Services SC Solutions has extensive expertise in developing finite element models (FEM) directly from 3-D Building Information Modeling (BIM) platforms such as Autodesk Revit and Tekla...

Thermal Analysis of Flare Containment Structure

SC Solutions delivered to East Bay Municipal Utility District (EBMUD) the results of a thermal analysis of the containment structure of their waste gas burners. EBMUD’s Main Wastewater Treatment Plant (MWWTP) in Oakland, CA operates a digester facility where...