{"product_id":"9789819245086","title":"Physics-Enhanced Neural Networks A Practical Guide to Architecture and Applications in Industrial AI","description":"\u003ch3\u003eInternational Series in Operations Research \u0026amp; Management Science\u003c\/h3\u003e\u003ch1\u003ePhysics-Enhanced Neural Networks\u003c\/h1\u003e\u003ch2\u003eA Practical Guide to Architecture and Applications in Industrial AI\u003c\/h2\u003e\u003ch3\u003eDmitry Mikhaylov | Evan Shellshear\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eBusiness \u0026amp; Economics \/ Production \u0026amp; Operations Management\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cp\u003eThis book shows how to weld deep learning to the laws that govern heavy-industry assets. Instead of treating sensor readings as detached numbers, the book teaches models to obey conservation of energy, fluid flow, and material stress. The storyline follows three recurring settings—a regional power grid, an offshore drilling head, and a 2000-hectare almond farm—to prove the concept across energy, manufacturing, and agriculture.\u003c\/p\u003e\n\u003cp\u003eReaders watch plain neural nets stumble when they predict outputs that break physics, then see PENN correct the error with loss penalties and custom layers that bake in the Navier-Stokes, Hooke, and Fourier equations. In benchmark tests the approach lifts predictive accuracy by up to 30 percent while trimming training energy by two-thirds. Case walk-throughs document a 25 percent cut in turbine downtime, a 50 percent reduction in drilling vibration peaks, and a 15 percent saving in irrigation power.\u003c\/p\u003e\n\u003cp\u003eSpecial features include code snippets ready to paste into PyTorch, side-by-side plots that compare raw and physics-aware outputs, and full-color schematics that trace data from sensor to dashboard. Each chapter ends with a one-page readiness checklist so teams can turn lessons into pilots without stalling in proof-of-concept limbo. A consistent American spelling palette avoids cross-Atlantic edits.\u003c\/p\u003e\n\u003cp\u003eBy the final page a plant manager, data scientist, or graduate student can design, train, and deploy a model that predicts real-world behavior without ignoring the rules of nature. The payoff is lower energy bills, fewer unplanned shutdowns, and a faster path to net-zero targets—results that speak to both profit and sustainability.\u003c\/p\u003e\n\u003cp\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003cp\u003eProf. Dr. Dmitry Mikhaylov is an internationally recognized scientist whose research focuses on applying physics-enhanced AI to solve complex industrial challenges. During his career, he has held academic positions at leading institutions such as the National University of Singapore (Singapore), Khalifa University (supervising professor) and served as a guest lecturer at the University of Sydney (Australia), Tufts University (USA), Vietnam National University (Vietnam), Dankook University (South Korea), and Tashkent State Agrarian University (Uzbekistan), contributing to the integration of AI into fields such as agriculture, healthcare, and logistics. He also serves as an invited UN expert for his contributions to AI-driven sustainable development. His research has led to multiple licensed inventions now commercialized across sectors including manufacturing, digital health, and environmental monitoring.\u003c\/p\u003e\r\n\u003cp\u003eDr Evan Shellshear is the author of multiple best selling books such as Innovation Tools which was a #1 bestseller on Amazon, Why Data Science Projects Fail (with the Director of Data Science at Walmart) which is a #1 bestseller in Australia and holds a PhD from the Nobel prize winning institute of Mathematical Economics in Bielefeld Germany and he serves as an adjunct professor at both University of Queensland and Queensland University of Technology in Australia with almost 30 scientific publications to his name. He is currently a Principal at BCG X. \u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e17 December 2026\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eSpringer Nature Singapore\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eSpringer\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9789819245086\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Springer Nature Singapore","offers":[{"title":"Default Title","offer_id":51322262487180,"sku":"9789819245086","price":98.99,"currency_code":"USD","in_stock":true}],"url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9789819245086","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}