35 citations · 48 across the 28 of their papers we have counts for
13 papers · 1 filter
Neural Residual Modeling for Scientific Data Compression under Guaranteed Error Bounds
Surya Majumder, Liangji Zhu, Sanjay Ranka +1
Lossy compression of scientific simulation data increasingly relies on learned, latent-space architectures such as Residual Vector Quantization (RVQ), which iteratively quantize a…
Enactor: From Traffic Simulators to Surrogate World Models
Yash Ranjan, Rahul Sengupta, Anand Rangarajan +1
Traffic microsimulators are widely used to evaluate road network performance under various ``what-if" conditions. However, the behavior models controlling the actions of the actors…
TGDT: A Temporal Graph-based Digital Twin for Urban Traffic Corridors
Nooshin Yousefzadeh, Rahul Sengupta, Jeremy Dilmore +1
Urban congestion at signalized intersections leads to significant delays, economic losses, and increased emissions. Existing deep learning models often lack spatial generalizabilit…
Guaranteed Conditional Diffusion: 3D Block-based Models for Scientific Data Compression
Jaemoon Lee, Xiao Li, Liangji Zhu +2
This paper proposes a new compression paradigm -- Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) -- for lossy scientific data compression. The framework is based o…
Foundation Model for Lossy Compression of Spatiotemporal Scientific Data
Xiao Li, Jaemoon Lee, Anand Rangarajan +1
We present a foundation model (FM) for lossy scientific data compression, combining a variational autoencoder (VAE) with a hyper-prior structure and a super-resolution (SR) module.…
Dynamic Graph Attention Networks for Travel Time Distribution Prediction in Urban Arterial Roads
Nooshin Yousefzadeh, Rahul Sengupta, Sanjay Ranka
Effective congestion management along signalized corridors is essential for improving productivity and reducing costs, with arterial travel time serving as a key performance metric…
Attention Based Machine Learning Methods for Data Reduction with Guaranteed Error Bounds
Xiao Li, Jaemoon Lee, Anand Rangarajan +1
Scientific applications in fields such as high energy physics, computational fluid dynamics, and climate science generate vast amounts of data at high velocities. This exponential…
Graph Attention Network for Lane-Wise and Topology-Invariant Intersection Traffic Simulation
Nooshin Yousefzadeh, Rahul Sengupta, Yashaswi Karnati +2
Traffic congestion has significant economic, environmental, and social ramifications. Intersection traffic flow dynamics are influenced by numerous factors. While microscopic traff…
MTDT: A Multi-Task Deep Learning Digital Twin
Nooshin Yousefzadeh, Rahul Sengupta, Yashaswi Karnati +2
Traffic congestion has significant impacts on both the economy and the environment. Measures of Effectiveness (MOEs) have long been the standard for evaluating traffic intersection…
Machine Learning Techniques for Data Reduction of Climate Applications
Xiao Li, Qian Gong, Jaemoon Lee +3
Scientists conduct large-scale simulations to compute derived quantities-of-interest (QoI) from primary data. Often, QoI are linked to specific features, regions, or time intervals…
Machine Learning Techniques for Data Reduction of CFD Applications
Jaemoon Lee, Ki Sung Jung, Qian Gong +5
We present an approach called guaranteed block autoencoder that leverages Tensor Correlations (GBATC) for reducing the spatiotemporal data generated by computational fluid dynamics…
Scalable Hybrid Learning Techniques for Scientific Data Compression
Tania Banerjee, Jong Choi, Jaemoon Lee +5
Data compression is becoming critical for storing scientific data because many scientific applications need to store large amounts of data and post process this data for scientific…
Expressing linear equality constraints in feedforward neural networks
Anand Rangarajan, Pan He, Jaemoon Lee +2
We seek to impose linear, equality constraints in feedforward neural networks. As top layer predictors are usually nonlinear, this is a difficult task if we seek to deploy standard…