30 citations · 135 across the 31 of their papers we have counts for
34 papers
Refined Grid Sampling for Efficient Implicit Neural Representations of Large-Scale Scientific Data
Guang Zhao, Xihaier Luo, Sean McCue +5
Implicit neural representations (INRs) offer a powerful framework for modeling high-dimensional physical fields, but their training remains inefficient because evaluating dense coo…
Efficient Neural Field Learning via Adaptive Coverage and Focused Sampling
Guang Zhao, Xihaier Luo, Huan-Hsin Tseng +4
Implicit neural representations (INRs) provide a flexible framework for modeling high-dimensional continuous fields, but their training is often inefficient due to uniform subsampl…
DANCE: Doubly Adaptive Neighborhood Conformal Estimation
Brandon R. Feng, Brian J. Reich, Daniel Beaglehole +7
The recent developments of complex deep learning models have led to unprecedented ability to accurately predict across multiple data representation types. Conformal prediction for…
Uncertainty-Calibrated Spatiotemporal Field Diffusion with Sparse Supervision
Kevin Valencia, Xihaier Luo, Shinjae Yoo +1
Physical fields are typically observed only at sparse, time-varying sensor locations, making forecasting and reconstruction ill-posed and uncertainty-critical. We present SOLID, a…
Dynamical Implicit Neural Representations
Yesom Park, Kelvin Kan, Thomas Flynn +4
Implicit Neural Representations (INRs) provide a powerful continuous framework for modeling complex visual and geometric signals, but spectral bias remains a fundamental challenge,…
OmniField: Conditioned Neural Fields for Robust Multimodal Spatiotemporal Learning
Kevin Valencia, Thilina Balasooriya, Xihaier Luo +2
Multimodal spatiotemporal learning on real-world experimental data is constrained by two challenges: within-modality measurements are sparse, irregular, and noisy (QA/QC artifacts)…
Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference
Ayush Khot, Miruna Oprescu, Maresa Schröder +2
Causal inference in spatial domains faces two intertwined challenges: (1) unmeasured spatial factors, such as weather, air pollution, or mobility, that confound treatment and outco…
TPCpp-10M: Simulated proton-proton collisions in a Time Projection Chamber for AI Foundation Models
Shuhang Li, Yi Huang, David Park +10
Scientific foundation models hold great promise for advancing nuclear and particle physics by improving analysis precision and accelerating discovery. Yet, progress in this field i…
FM4NPP: A Scaling Foundation Model for Nuclear and Particle Physics
David Park, Shuhang Li, Yi Huang +9
Large language models have revolutionized artificial intelligence by enabling large, generalizable models trained through self-supervision. This paradigm has inspired the developme…
STACI: Spatio-Temporal Aleatoric Conformal Inference
Brandon R. Feng, David Keetae Park, Xihaier Luo +3
Fitting Gaussian Processes (GPs) provides interpretable aleatoric uncertainty quantification for estimation of spatio-temporal fields. Spatio-temporal deep learning models, while s…
Explorable INR: An Implicit Neural Representation for Ensemble Simulation Enabling Efficient Spatial and Parameter Exploration
Yi-Tang Chen, Haoyu Li, Neng Shi +3
With the growing computational power available for high-resolution ensemble simulations in scientific fields such as cosmology and oceanology, storage and computational demands pre…
SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields
David Keetae Park, Xihaier Luo, Guang Zhao +3
Spatiotemporal learning is challenging due to the intricate interplay between spatial and temporal dependencies, the high dimensionality of the data, and scalability constraints. T…
Generalizable Implicit Neural Representations via Parameterized Latent Dynamics for Baroclinic Ocean Forecasting
Guang Zhao, Xihaier Luo, Seungjun Lee +7
Mesoscale ocean dynamics play a critical role in climate systems, governing heat transport, hurricane genesis, and drought patterns. However, simulating these processes at high res…
GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding
Miruna Oprescu, David K. Park, Xihaier Luo +2
Estimating causal effects from spatiotemporal observational data is essential in public health, environmental science, and policy evaluation, where randomized experiments are often…
Evidential Deep Learning for Probabilistic Modelling of Extreme Storm Events
Ayush Khot, Xihaier Luo, Ai Kagawa +1
Uncertainty quantification (UQ) methods play an important role in reducing errors in weather forecasting. Conventional approaches in UQ for weather forecasting rely on generating a…
Efficient Compression of Sparse Accelerator Data Using Implicit Neural Representations and Importance Sampling
Xihaier Luo, Samuel Lurvey, Yi Huang +3
High-energy, large-scale particle colliders in nuclear and high-energy physics generate data at extraordinary rates, reaching up to terabyte and several petabytes per second, r…
Variable Rate Neural Compression for Sparse Detector Data
Yi Huang, Yeonju Go, Jin Huang +9
High-energy large-scale particle colliders generate data at extraordinary rates. Developing real-time high-throughput data compression algorithms to reduce data volume and meet the…
Implicit Neural Representations for Simultaneous Reduction and Continuous Reconstruction of Multi-Altitude Climate Data
Alif Bin Abdul Qayyum, Xihaier Luo, Nathan M. Urban +2
The world is moving towards clean and renewable energy sources, such as wind energy, in an attempt to reduce greenhouse gas emissions that contribute to global warming. To enhance…
Hierarchical Neural Operator Transformer with Learnable Frequency-aware Loss Prior for Arbitrary-scale Super-resolution
Xihaier Luo, Xiaoning Qian, Byung-Jun Yoon
In this work, we present an arbitrary-scale super-resolution (SR) method to enhance the resolution of scientific data, which often involves complex challenges such as continuity, m…
Studying the Impact of Latent Representations in Implicit Neural Networks for Scientific Continuous Field Reconstruction
Wei Xu, Derek Freeman DeSantis, Xihaier Luo +5
Learning a continuous and reliable representation of physical fields from sparse sampling is challenging and it affects diverse scientific disciplines. In a recent work, we present…
Multi-modal Representation Learning for Cross-modal Prediction of Continuous Weather Patterns from Discrete Low-Dimensional Data
Alif Bin Abdul Qayyum, Xihaier Luo, Nathan M. Urban +2
World is looking for clean and renewable energy sources that do not pollute the environment, in an attempt to reduce greenhouse gas emissions that contribute to global warming. Win…
Continuous Field Reconstruction from Sparse Observations with Implicit Neural Networks
Xihaier Luo, Wei Xu, Yihui Ren +2
Reliably reconstructing physical fields from sparse sensor data is a challenge that frequently arises in many scientific domains. In practice, the process generating the data often…
Density Estimation via Measure Transport: Outlook for Applications in the Biological Sciences
Vanessa Lopez-Marrero, Patrick R. Johnstone, Gilchan Park +1
One among several advantages of measure transport methods is that they allow for a unified framework for processing and analysis of data distributed according to a wide class of pr…
Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge
Gilchan Park, Byung-Jun Yoon, Xihaier Luo +3
Background: Identification of the interactions and regulatory relations between biomolecules play pivotal roles in understanding complex biological systems and the mechanisms under…