Search Results for author:
Found 9 papers, 2 papers with code
Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels
To overcome the computational challenge of kernel regression, we place the function values on a mesh and induce a Kronecker product construction, and we use tensor algebra to enable efficient computation and optimization.
Seeking the Yield Barrier: High-Dimensional SRAM Evaluation Through Optimal Manifold
We then generalize it with infinite components and derive the novel optimal manifold concept, which bridges the surrogate-based and importance sampling (IS) yield estimation methods.
Differentiable Multi-Fidelity Fusion: Efficient Learning of Physics Simulations with Neural Architecture Search and Transfer Learning
With rapid progress in deep learning, neural networks have been widely used in scientific research and engineering applications as surrogate models.
GAR: Generalized Autoregression for Multi-Fidelity Fusion
In many scientific research and engineering applications where repeated simulations of complex systems are conducted, a surrogate is commonly adopted to quickly estimate the whole system.
High-Dimensional Yield Estimation using Shrinkage Deep Features and Maximization of Integral Entropy Reduction
Despite the fast advances in high-sigma yield analysis with the help of machine learning techniques in the past decade, one of the main challenges, the curse of dimensionality, which is inevitable when dealing with modern large-scale circuits, remains unsolved.
Enhanced Gaussian Process Dynamical Models with Knowledge Transfer for Long-term Battery Degradation Forecasting
To improve the accuracy of predictive estimates, especially early in the battery lifetime, a number of algorithms have incorporated features that are available from data collected by battery management systems.
E-LMC: Extended Linear Model of Coregionalization for Spatial Field Prediction
Physical simulations based on partial differential equations typically generate spatial fields results, which are utilized to calculate specific properties of a system for engineering design and optimization.
BoA-PTA, A Bayesian Optimization Accelerated Error-Free SPICE Solver
One of the greatest challenges in IC design is the repeated executions of computationally expensive SPICE simulations, particularly when highly complex chip testing/verification is involved.
Residual Gaussian Process: A Tractable Nonparametric Bayesian Emulator for Multi-fidelity Simulations
The resulting model is equipped with a closed-form solution for the predictive posterior, making it applicable to advanced, high-dimensional tasks that require uncertainty estimation.
