UiO Realfagsbiblioteket | Exploring System Dynamics in the Natural World with AI - William Gilpin @realfagsbiblioteket | Uploaded October 2024 | Updated October 2024, 3 minutes ago.
William Gilpin (UT Austin) - Model scale versus domain knowledge in long-term forecasting of chaos
Welcome to a conference on discovery and prediction in complex geo systems. AI/ML shows promise for accelerating Bayesian inversions, is able to capture and identify controlling features of high fidelity models, and appears to improve complex system analysis, including extending predictive horizons, for reasons that are not fully understood. This conference focuses on efforts to advance the use of AI/ML to understand physical processes in the geosciences and adjacent fields.
AI/ML shows promise for accelerating Bayesian inversions, is able to capture and identify aspects of the dynamics of high fidelity models, and appears to improve complex system analysis, including extending predictive horizons, for reasons that are not fully understood. This has been demonstrated across the physical sciences including for geophysical modeling, fracture mechanics, and climate and weather prediction.
This conference focuses on efforts to advance the use of AI/ML to understand physical processes in the geosciences and adjacent fields, and brings together practitioners and theorists from academia and industry for an open exchange of current results and discussions of future strategies.
A central goal is to encourage diversity and collaboration between different disciplines, all through AI/ML practice, and to identify new connections between applications in fields, from networks to earthquakes, and turbulence to chaos.
https://www.ub.uio.no/english/courses-events/events/mednat/ureal/2024/240930conference.html
William Gilpin (UT Austin) - Model scale versus domain knowledge in long-term forecasting of chaos
Welcome to a conference on discovery and prediction in complex geo systems. AI/ML shows promise for accelerating Bayesian inversions, is able to capture and identify controlling features of high fidelity models, and appears to improve complex system analysis, including extending predictive horizons, for reasons that are not fully understood. This conference focuses on efforts to advance the use of AI/ML to understand physical processes in the geosciences and adjacent fields.
AI/ML shows promise for accelerating Bayesian inversions, is able to capture and identify aspects of the dynamics of high fidelity models, and appears to improve complex system analysis, including extending predictive horizons, for reasons that are not fully understood. This has been demonstrated across the physical sciences including for geophysical modeling, fracture mechanics, and climate and weather prediction.
This conference focuses on efforts to advance the use of AI/ML to understand physical processes in the geosciences and adjacent fields, and brings together practitioners and theorists from academia and industry for an open exchange of current results and discussions of future strategies.
A central goal is to encourage diversity and collaboration between different disciplines, all through AI/ML practice, and to identify new connections between applications in fields, from networks to earthquakes, and turbulence to chaos.
https://www.ub.uio.no/english/courses-events/events/mednat/ureal/2024/240930conference.html
