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https://cwi-nl-zoom.zoom.us/j/82597071430pwd=bVQaeq758rFjDPD57ZzgiiYmr2JlCu.1
Meeting ID: 825 9707 1430 Passcode: 196359
Title: Smart AI, Not Just Accurate AI: Towards Sustainable Scientific Machine Learning
Abstract: AI is getting very good at solving scientific problems. It can predict how physical systems behave. Methods like Physics Informed Neural Networks and operator learning are widely used for this. But there is a hidden cost. These models need a lot of computation. This leads to high energy use and carbon emissions. Most of the time, we only measure accuracy. We ignore the environmental impact. In this talk, we ask a simple question. What if we measure both accuracy and carbon cost? We introduce EcoL2. It is a new metric for scientific machine learning. It measures how well a model performs and how much carbon it produces across its full lifecycle. Our results show something important. Two models can have similar accuracy. But one can use much more energy than the other. This is true for PINNs and operator learning methods. Accuracy alone is not enough. We need better ways to compare models. This talk encourages a new direction. Build models that are accurate, efficient, and environmentally responsible.
