Discovery and Optimization of Complex, Concentrated Alloys via Multi-Objective Bayesian Optimization and Multi-Scale Modeling
Presenting work on bayesian optimization of complex alloys
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Presenting work on bayesian optimization of complex alloys
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The PIP-II linac will enable >1.2 MW beam power for DUNE, requiring unprecedented operational reliability across its warm front-end (RFQ, MEBT) and five distinct SRF sections operating at 162.5/325/650 MHz. We present a comprehensive digital twin framework uniquely combining a fully differentiable fast beam transport code with neural network surrogates trained on high-fidelity PIC simulations, capturing space charge and nonlinear dynamics beyond traditional envelope codes while achieving 10⁴ speedup at <1% accuracy. End-to-end differentiability enables gradient-based optimization across 500+ parameters simultaneously previously impossible with conventional tools while the model incorporates static/dynamic errors and serves as a virtual commissioning platform for diverse hardware integration. The framework facilitates reinforcement learning for pulsed/CW mode transitions, predictive maintenance through anomaly detection, and autonomous tuning algorithm development with real-time execution capability. Validation against physics simulations shows excellent agreement for the front-end, with initial results demonstrating potential for 30% commissioning time reduction and proactive fault mitigation, providing a scalable blueprint for operating next-generation high-intensity accelerators.
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This award resulted in a better characterization of shallow convection in the marine boundary layer during cold-air outbreaks. This was done through an examination of the coupling between marine boundary layer circulations that control cloud organization and cloud microphysical processes.
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ICACC 2022, Virtual, January 22-27, 2022
National Risk Assessment Partnership Annual Technical Meeting, Virtual, May 3-4, 2022