NEUP DECP-28434: Physics-Informed Machine Learning to Accelerate Process Modeling in Additive Manufacturing of Structure Materials for Nuclear Industries

The goal of this research is directed toward developing scientific and formalized physics-informed data-driven techniques toward accelerating the generation of both forward and inverse scaling laws that can be transferred across material systems or manufacturing processes to understand the fundamental linkages between processing and the properties of interest in metal additive manufacturing (AM).

Additional Info

Field	Value
Awarded Institution	Pennsylvania State University
PI	Amrita Basak