26 citations · 49 across the 8 of their papers we have counts for
5 papers · 1 filter
End-to-End Differentiable Diffusion Conditioning for Physics-Informed Optimization
Ricardo Luna Gutierrez, Vineet Gundecha, Rahman Ejaz +5
Optimizing high-dimensional physical systems under expensive evaluations and complex constraints is a central challenge in science and engineering. In offline model-based optimizat…
Decomposition-Guided Diffusion Language Models for Inertial Confinement Fusion Prediction
Xiang Zhang, Varchas Gopalaswamy, Rahman Ejaz +2
Inertial confinement fusion (ICF) is a leading pathway toward clean energy, but each shot at the National Ignition Facility costs on the order of one million dollars, making accura…
Human-in-the-Loop Meta Bayesian Optimization for Fusion Energy and Scientific Applications
Ricardo Luna Gutierrez, Sahand Ghorbanpour, Ejaz Rahman +6
Inertial Confinement Fusion (ICF) holds transformative promise for sustainable, near-limitless clean energy, yet remains constrained by prohibitively high costs and limited experim…
BayMOTH: Bayesian optiMizatiOn with meTa-lookahead -- a simple approacH
Rahman Ejaz, Varchas Gopalaswamy, Ricardo Luna +5
Bayesian optimization (BO) has for sequential optimization of expensive black-box functions demonstrated practicality and effectiveness in many real-world settings. Meta-Bayesian o…
Can Kans (re)discover predictive models for Direct-Drive Laser Fusion?
Rahman Ejaz, Varchas Gopalaswamy, Riccardo Betti +2
The domain of laser fusion presents a unique and challenging predictive modeling application landscape for machine learning methods due to high problem complexity and limited train…