3 papers
cs.LG2026
D-JEPA: Design-Recoverable JEPA Representation with Swappable Physics Decoders
Nitin Nagesh Kulkarni, Aashwin Anand Mishra, Yin Yu +1
Joint-Embedding Predictive Architectures (JEPAs) provide a framework for learning compact representations without directly reconstructing high-dimensional observations. However, in…
cs.LG2026
A Data Fusion Framework for Grounding Aerospace Surrogate Model via Experimental Wind-Tunnel Observations
Nitin Nagesh Kulkarni, Dheeraj Vemula, Yin Yu +2
Aerodynamic surrogate models trained on high-fidelity CFD data reproduce numerical predictions of both scalar outputs and entire fields accurately, yet their predictive fidelity is…
cs.AI2025
PKG-DPO: Optimizing Domain-Specific AI systems with Physics Knowledge Graphs and Direct Preference Optimization
Nitin Nagesh Kulkarni, Bryson Wilcox, Max Sawa +1
Advancing AI systems in scientific domains like physics, materials science, and engineering calls for reasoning over complex, multi-physics phenomena while respecting governing pri…