6 papers
When Language Models Misinterpret Uncertainty in Earth System Mechanism Attribution
Pouria Behnoudfar, Nan Chen
Mechanistic attribution in Earth system science requires quantifying uncertainty and communicating its implications. Here, we develop an uncertainty-aware mechanistic attribution p…
QKAN-LSTM: Quantum-inspired Kolmogorov-Arnold Long Short-term Memory
Yu-Chao Hsu, Jiun-Cheng Jiang, Chun-Hua Lin +5
Long short-term memory (LSTM) models are a particular type of recurrent neural networks (RNNs) that are central to sequential modeling tasks in domains such as urban telecommunicat…
Bridging Idealized and Operational Models: An Explainable AI Framework for Earth System Emulators
Pouria Behnoudfar, Charlotte Moser, Marc Bocquet +2
Computer models are indispensable tools for understanding the Earth system. While high-resolution operational models have achieved many successes, they exhibit persistent biases, p…
RL-DAUNCE: Reinforcement Learning-Driven Data Assimilation with Uncertainty-Aware Constrained Ensembles
Pouria Behnoudfar, Nan Chen
Machine learning has become a powerful tool for enhancing data assimilation. While supervised learning remains the standard method, reinforcement learning (RL) offers unique advant…
Minimum Reduced-Order Models via Causal Inference
Nan Chen, Honghu Liu
Constructing sparse, effective reduced-order models (ROMs) for high-dimensional dynamical data is an active area of research in applied sciences. In this work, we study an efficien…
Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments
Hongtao Zhu, Sizhe Zhang, Yang Su +2
In the domain of causal inference research, the prevalent potential outcomes framework, notably the Rubin Causal Model (RCM), often overlooks individual interference and assumes in…