The field of uncertainty quantification and optimization is rapidly advancing, with a focus on developing efficient and accurate methods for complex systems. Recent research has highlighted the importance of combining different techniques, such as multi-level Monte Carlo sampling and parallel-in-time integration, to accelerate computations while maintaining accuracy. Additionally, bi-fidelity methods and Bayesian optimization are being explored for their potential to improve the efficiency and effectiveness of uncertainty quantification and optimization. Notable papers in this area include: A paper on Multi-Level Monte Carlo sampling with Parallel-in-Time Integration for Uncertainty Quantification in Electric Machine Simulation, which demonstrates a speedup of 12-45% compared to traditional methods. A paper on Bayesian Neural Network Surrogates for Bayesian Optimization of Carbon Capture and Storage Operations, which shows the potential of using novel stochastic models for Bayesian optimization in complex systems.
Advancements in Uncertainty Quantification and Optimization
Sources
Multi-Level Monte Carlo sampling with Parallel-in-Time Integration for Uncertainty Quantification in Electric Machine Simulation
A Bi-fidelity numerical method for velocity discretization of Boltzmann equations
Efficient numerical methods for the uncertain Boltzmann equation based on a hybrid solver
Bayesian Neural Network Surrogates for Bayesian Optimization of Carbon Capture and Storage Operations
Multi-fidelity Bayesian Data-Driven Design of Energy Absorbing Spinodoid Cellular Structures
A Structure-Preserving Rational Integrator for the Replicator Dynamics on the Probability Simplex
Bayesian Optimization applied for accelerated Virtual Validation of the Autonomous Driving Function
Stabilization of Age-Structured Competing Populations