Tag: Scientific Computing
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AI Engines for Extreme-Edge Scientific Computing: Design Tips
Explore design rules and optimizations for deploying neural networks on AI Engines in extreme-edge scientific computing with low latency and high throughpu...
AI-Driven Workflow for Scalable Detector Design Optimization
Optimize detector design with AI-assisted workflows using PanDA and distributed computing for scalable, efficient multi-objective optimization.
DSO: Dual-Scale Neural Operators for Accurate Fluid Forecasts
Discover how Dual-Scale Neural Operators improve long-term fluid dynamics forecasting with enhanced stability and precision in turbulent flow predictions.
CGRA4ML: Neural Networks for Scientific Edge Computing
Discover CGRA4ML, an open-source hardware/software framework enabling efficient neural network implementation for scientific edge computing applications.
Deep Neural Networks as Dynamical Systems in Physics Learning
Explore how deep neural networks function as discrete dynamical systems and their impact on physics-informed learning and scientific computing.