This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intellige...This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection techniques.Traditional models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical processes.However,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world applicability.In contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical knowledge.ML techniques have shown promise in addressing Earth science-related questions.Nevertheless,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into geoscience.The integration of physics-based and data-driven methodologies into hybrid models presents an alternative paradigm.These models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data requirements.This review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and geoscience.It examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in geoscience.The paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.展开更多
Modeling the outbreak of a novel epidemic,such as coronavirus disease 2019(COVID-19),is crucial for estimating its dynamics,predicting future spread and evaluating the effects of different interventions.However,there ...Modeling the outbreak of a novel epidemic,such as coronavirus disease 2019(COVID-19),is crucial for estimating its dynamics,predicting future spread and evaluating the effects of different interventions.However,there are three issues that make this modeling a challenging task:uncertainty in data,roughness in models,and complexity in programming.We addressed these issues by presenting an interactive individual-based simulator,which is capable of modeling an epidemic through multisource information fusion.展开更多
基金supported by National Natural Science Foundation of China(T2225019,41925007,62372470,U21A2013,42201415,42022054,42241109,42077156,52121006,42090014,and 42325107)the National Key R&D Programme of China(2022YFF0500)+2 种基金the Youth Innovation Promotion Association CAS(2023112)the Strategic Priority Research Program of CAS(XDA23090303)the RECLAIM Network Plus(EP/W034034/1).
文摘This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection techniques.Traditional models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical processes.However,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world applicability.In contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical knowledge.ML techniques have shown promise in addressing Earth science-related questions.Nevertheless,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into geoscience.The integration of physics-based and data-driven methodologies into hybrid models presents an alternative paradigm.These models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data requirements.This review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and geoscience.It examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in geoscience.The paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.
基金This work is partially supported by NSFC no.61902376,NSFC no.61602487,and NSFC no.81701067.
文摘Modeling the outbreak of a novel epidemic,such as coronavirus disease 2019(COVID-19),is crucial for estimating its dynamics,predicting future spread and evaluating the effects of different interventions.However,there are three issues that make this modeling a challenging task:uncertainty in data,roughness in models,and complexity in programming.We addressed these issues by presenting an interactive individual-based simulator,which is capable of modeling an epidemic through multisource information fusion.
