USNCCM18 Semi-Plenary: Vipin Kumar, University of Minnesota скачать в хорошем качестве

USNCCM18 Semi-Plenary: Vipin Kumar, University of Minnesota

AI Meets Science: Knowledge-Guided Machine Learning for Accelerating Discovery Vipin Kumar, University of Minnesota Inspired by the remarkable success of machine learning (ML) in fields such as computer vision and language modeling, the scientific community is increasingly excited to harness its potential for addressing societal challenges. However, realizing this potential requires a paradigm shift in data-intensive scientific discovery, as ‘black box’ ML models often fail to generalize to unseen scenarios and may produce results that conflict with established scientific understanding of the underlying phenomena. This talk presents an overview of a new generation of machine learning algorithms, where scientific knowledge is deeply integrated in the design and training of machine learning models to accelerate scientific discovery. These knowledge-guided machine learning (KGML) techniques are fundamentally more powerful than standard machine learning approaches, and are particularly relevant for scientific and engineering problems that are traditionally addressed via process-guided (also called mechanistic or first principle-based) models, but whose solutions are hampered by incomplete or inaccurate knowledge of physics or underlying processes. While this talk will illustrate the potential of the KGML paradigm in the context of environmental problems (e.g., Ecology, Hydrology, Agronomy, climate science), the paradigm has the potential to greatly advance the pace of discovery in any discipline where mechanistic models are used.