3 citations · 9 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data
Majid Farhadloo, Arun Sharma, Alexey Leontovich +2
Given multi-type point maps from different place-types (e.g., tumor regions), our objective is to develop a classifier trained on the source place-type to accurately distinguish be…
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…