Brief

A new research paper introduces SAFE-NET, a novel Single-layered Adaptive Feature Engineering NETwork designed to enhance Physics-Informed Neural Networks (PINNs). This method significantly reduces error rates and training time compared to existing PINN approaches by employing Fourier features, a simplified single hidden layer architecture, and an optimized training process. SAFE-NET demonstrates substantial efficiency gains, using fewer parameters and achieving faster epoch times while maintaining comparable accuracy, challenging the notion that complex deep learning architectures are always necessary for scientific applications. AI

Researchers have introduced ShapeBench, a new open-source benchmark designed to standardize evaluations in aerodynamic shape optimization. This benchmark includes 103 tasks across eight shape categories, featuring validated surrogates for rapid testing and optional high-fidelity CFD pipelines for verification. ShapeBench aims to enable fair comparisons between various optimization methods, including classical, general-purpose, and LLM-driven approaches, by using a consistent budget metric and highlighting the variance in optimizer performance across different tasks. AI