From Grey-Box to Green-Box: When can Physics-Informed Machine Learning Reduce Carbon Footprints in Structural Health Monitoring?
cs.LG
Submitted: 2026-09-27
Updated: 2026-09-27
Code: https://github.com/mlco2/codecarbon
Terminology
Sources
- Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models
- Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements
- Quantifying the Carbon Emissions of Machine Learning
- Learning both Weights and Connections for Efficient Neural Networks
- A Survey of Quantization Methods for Efficient Neural Network Inference
- Post Training Quantization of Large Language Models with Microscaling Formats
- DeepSeek-V3 Technical Report
- DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models
- Understanding and mitigating gradient pathologies in physics-informed neural networks
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