An adaptive anisotropic composite quadrature strategy combined with refresh-based training narrows the gap between training and reference losses in neural residual minimization for PDEs while using quadrature points more efficiently.
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UNVERDICTED 4representative citing papers
Theta-regularized Kriging penalizes the theta hyperparameter in Gaussian stochastic processes using Lasso, Ridge, or Elastic-net, yielding higher accuracy and stability than prior penalized Kriging variants on numerical tests and engineering cases.
Multi-agent LLM system applies set-based design and Conditional Value-at-Risk to explore and risk-filter airfoil designs with human manager coordination.
Two numerical models (RSI-based and ray-tracing) for ultrasonic wave propagation across interfaces are developed, implemented with QMC integration, and compared for computational efficiency across use cases.
citing papers explorer
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Adaptive anisotropic composite quadratures for residual minimisation in neural PDE approximations
An adaptive anisotropic composite quadrature strategy combined with refresh-based training narrows the gap between training and reference losses in neural residual minimization for PDEs while using quadrature points more efficiently.
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Theta-regularized Kriging: Modelling and Algorithms
Theta-regularized Kriging penalizes the theta hyperparameter in Gaussian stochastic processes using Lasso, Ridge, or Elastic-net, yielding higher accuracy and stability than prior penalized Kriging variants on numerical tests and engineering cases.
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Agentic Risk-Aware Set-Based Engineering Design
Multi-agent LLM system applies set-based design and Conditional Value-at-Risk to explore and risk-filter airfoil designs with human manager coordination.
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Efficient numerical frameworks for modelling ultrasonic beams propagating across interfaces
Two numerical models (RSI-based and ray-tracing) for ultrasonic wave propagation across interfaces are developed, implemented with QMC integration, and compared for computational efficiency across use cases.