Tag: Machine Learning
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HydroGym trains, assesses AI for actively controlling fluid dynamics
With more than 60 environments, the simulated proving ground aims to speed the development of AI controllers that optimize drag, lift, noise and heat management.
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Key structures to metallic glass stability revealed with machine learning
Using the second-nearest neighboring atoms to predict metallic glass stability can help researchers more accurately model the disordered solid with strong, elastic properties.
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Providing the Artemis mission with solar radiation forecasts
Machine-learning and physics-based models developed at U-M will warn NASA when solar particle radiation could become hazardous up to 24 hours in advance.
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How to improve AI energy efficiency with open-source tools: Q&A with Mosharaf Chowdhury
Any company could use our tools to measure and optimize their AI models and reduce AI energy use.
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Letting atomic simulations learn from phase diagrams
Ten times more efficient than previous methods, a new machine learning method builds a two-way connection between atomic simulation and experimental data.
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AI tool predicts battery cycle life with just a few days’ data
A ‘learner,’ ‘interpreter’ and ‘oracle’ work together with minimal experiments to draw parallels between historical data and new battery designs.
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AI supports home-based balance training
New machine learning model draws data from wearable sensors to predict how a physical therapist would assess balance training performance.
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Interpretable machine learning to accelerate nanocatalyst discovery
A fast and accurate surrogate model screens over 10,000 possible metal-oxide supports for a platinum nanocatalyst to prevent sintering under high temperatures.
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AI for studying turbulence: A fresh look at an unsolved physics problem
Explainable AI helps find key drivers of turbulence, offering new insights that could improve flight safety and industrial efficiency.
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Between rain and snow, machine learning finds 9 precipitation types
Leveraging 1.5M minutes of precipitation data and a nonlinear method to handle complex relationships between variables, the team created a new classification system
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Quantum chemistry: Making key simulation approach more accurate
Density functional theory is limited by a mystery at its heart: the universal exchange-correlation functional. U-M researchers are trying to uncover it.
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AI system discovers visual categories while adapting to new contexts
Open ad-hoc categorization approach combines language guidance with visual clustering to learn contextualized features for flexible image interpretation.