𝐈𝐭'𝐬 𝐭𝐡𝐞 𝐟𝐢𝐫𝐬𝐭 𝐨𝐟 𝐭𝐡𝐞 𝐦𝐨𝐧𝐭𝐡 𝐚𝐧𝐝 𝐚 𝐠𝐫𝐞𝐚𝐭 𝐭𝐢𝐦𝐞 𝐭𝐨 𝐝𝐞𝐛𝐮𝐭 𝐦𝐲 𝐬𝐢𝐝𝐞 𝐩𝐫𝐨𝐣𝐞𝐜𝐭. The project, Simulator-Informed Neural Networks (𝐬𝐢𝐦𝐢𝐧𝐧𝐬.𝐜𝐨𝐦), explores how to leverage the speed and flexibility of machine-learning representations of physics-based models. I am interested in extending the utility of simulation models by modernizing essential methods and algorithms. 𝐍𝐨 𝐭𝐢𝐜𝐤𝐞𝐭 𝐭𝐨 𝐭𝐡𝐞 𝐇𝐘𝐏𝐄 𝐓𝐑𝐀𝐈𝐍 𝐢𝐬 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐝. I explore how established methods can be accelerated and robustified with a speedy but accurate ML surrogate model. There are already a few pages available for everyone to read: • Surrogate Model-based Optimization • Optimization under Uncertainty • Parameter Estimation via Profile Likelihoods • Anomaly Detection I will be posting updates periodically as this project progresses and evolves. Until the next post, always remember to: 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 𝑆𝑝𝑒𝑒𝑑. 𝐸𝑚𝑏𝑟𝑎𝑐𝑒 𝑈𝑛𝑐𝑒𝑟𝑡𝑎𝑖𝑛𝑡𝑦. 𝐶𝑎𝑝𝑡𝑢𝑟𝑒 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒.
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