Search Results for author:
Found 13 papers, 3 papers with code
Prescribed Fire Modeling using Knowledge-Guided Machine Learning for Land Management
Traditional ML methods used for fire modeling offer computational speedup but struggle with physically inconsistent predictions, biased predictions due to class imbalance, biased estimates for fire spread metrics (e. g., burned area, rate of spread), and generalizability in out-of-distribution wind conditions.
Realization of Causal Representation Learning to Adjust Confounding Bias in Latent Space
Causal DAGs(Directed Acyclic Graphs) are usually considered in a 2D plane.
Mini-Batch Learning Strategies for modeling long term temporal dependencies: A study in environmental applications
To address this issue, we further propose a new strategy which augments a training segment with an initial value of the target variable from the timestep right before the starting of the training segment.
Incorporating Causal Effects into Deep Learning Predictions on EHR Data
Electronic Health Records (EHR) data analysis plays a crucial role in healthcare system quality.
Physics-Guided Recurrent Graph Networks for Predicting Flow and Temperature in River Networks
This paper proposes a physics-guided machine learning approach that combines advanced machine learning models and physics-based models to improve the prediction of water flow and temperature in river networks.
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
There is a growing consensus that solutions to complex science and engineering problems require novel methodologies that are able to integrate traditional physics-based modeling approaches with state-of-the-art machine learning (ML) techniques.
Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles
Specifically, we show that a PGRNN can improve prediction accuracy over that of physics-based models, while generating outputs consistent with physical laws.
Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles
This paper proposes a physics-guided recurrent neural network model (PGRNN) that combines RNNs and physics-based models to leverage their complementary strengths and improve the modeling of physical processes.
Mining Novel Multivariate Relationships in Time Series Data Using Correlation Networks
In many domains, there is significant interest in capturing novel relationships between time series that represent activities recorded at different nodes of a highly complex system.
Physics Guided Recurrent Neural Networks For Modeling Dynamical Systems: Application to Monitoring Water Temperature And Quality In Lakes
In this paper, we introduce a novel framework for combining scientific knowledge within physics-based models and recurrent neural networks to advance scientific discovery in many dynamical systems.
Mining Electronic Health Records: A Survey
In this manuscript, we provide a structured and comprehensive overview of data mining techniques for modeling EHR data.
Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data
Theory-guided data science (TGDS) is an emerging paradigm that aims to leverage the wealth of scientific knowledge for improving the effectiveness of data science models in enabling scientific discovery.
Causal Inference in Observational Data
We also evaluated our causal rule mining framework on the Electronic Health Records (EHR) data of a large cohort of patients from Mayo Clinic and showed that the patterns we extracted are sufficiently rich to explain the controversial findings in the medical literature regarding the effect of a class of cholesterol drugs on Type-II Diabetes Mellitus (T2DM).
