2 citations · 3 across the 5 of their papers we have counts for
5 papers
Towards Fine-Tuning-Based Site Calibration for Knowledge-Guided Machine Learning: A Summary of Results
Ruolei Zeng, Arun Sharma, Shuai An +5
Accurate and cost-effective quantification of the agroecosystem carbon cycle at decision-relevant scales is essential for climate mitigation and sustainable agriculture. However, b…
Towards Physics-informed Diffusion for Anomaly Detection in Trajectories
Arun Sharma, Mingzhou Yang, Majid Farhadloo +3
Given trajectory data, a domain-specific study area, and a user-defined threshold, we aim to find anomalous trajectories indicative of possible GPS spoofing (e.g., fake trajectory)…
Towards Physics-Guided Foundation Models
Majid Farhadloo, Arun Sharma, Mingzhou Yang +3
Traditional foundation models are pre-trained on broad datasets to reduce the training resources (e.g., time, energy, labeled samples) needed for fine-tuning a wide range of downst…
Spatial Distribution-Shift Aware Knowledge-Guided Machine Learning
Arun Sharma, Majid Farhadloo, Mingzhou Yang +3
Given inputs of diverse soil characteristics and climate data gathered from various regions, we aimed to build a model to predict accurate land emissions. The problem is important…
Eco-PiNN: A Physics-informed Neural Network for Eco-toll Estimation
Yan Li, Mingzhou Yang, Matthew Eagon +4
The eco-toll estimation problem quantifies the expected environmental cost (e.g., energy consumption, exhaust emissions) for a vehicle to travel along a path. This problem is impor…