Extends structural identifiability analysis to functional components of differential equation models and characterizes conditions for unique recovery using differential algebra techniques.
ACM Computing Surveys55(4), 1–37 (2022)
7 Pith papers cite this work, alongside 640 external citations. Polarity classification is still indexing.
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Pith papers citing it
640
external citations · OpenAlex
representative citing papers
An active learning method based on E-SINDy identifies governing ODEs and PDEs accurately with significantly fewer data samples than random sampling across tested systems.
AMIGO is an end-to-end differentiable forward model of JWST AMI that corrects detector systematics to recover high-precision astrometry and detect close high-contrast companions.
For small AWJM process data, treating statistical curation as competing hypotheses, using multi-fold evaluation, and residual physics with GPs yields more stable rankings and calibrated uncertainty than single-split pure ML.
A PINN fuses NOAA Coral Reef Watch SST with sparse loggers via the 1D heat equation to generate depth-resolved temperatures and Degree Heating Day profiles with 0.25-1.38°C RMSE at unseen depths.
WeatherRobustBus injects hourly weather into real bus blocks, couples a physics backbone with a bounded monotone residual ensemble, and shows a policy reducing cold-wave failure probability from 0.759 to 0.112.
An adaptive multi-particle SDE framework using Euler-Maruyama discretization and Girsanov change of measure is developed for non-linear forecasting of marine engine parameters, claiming superior multi-step stability and efficiency over VARIMA.
citing papers explorer
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Structural functional identifiability and model discovery in differential equation models
Extends structural identifiability analysis to functional components of differential equation models and characterizes conditions for unique recovery using differential algebra techniques.
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How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit
An active learning method based on E-SINDy identifies governing ODEs and PDEs accurately with significantly fewer data samples than random sampling across tested systems.
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AMIGO: a Data-Driven Calibration of the JWST Interferometer
AMIGO is an end-to-end differentiable forward model of JWST AMI that corrects detector systematics to recover high-precision astrometry and detect close high-contrast companions.
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Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling
For small AWJM process data, treating statistical curation as competing hypotheses, using multi-fold evaluation, and residual physics with GPs yields more stable rankings and calibrated uncertainty than single-split pure ML.
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Depth-Resolved Coral Reef Thermal Fields from Satellite SST and Sparse In-Situ Loggers Using Physics-Informed Neural Networks
A PINN fuses NOAA Coral Reef Watch SST with sparse loggers via the 1D heat equation to generate depth-resolved temperatures and Degree Heating Day profiles with 0.25-1.38°C RMSE at unseen depths.
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When the Timetable Breaks: Physics-Anchored Scientific Machine Learning for Cold-Wave-Robust Battery-Electric Bus Operations
WeatherRobustBus injects hourly weather into real bus blocks, couples a physics backbone with a bounded monotone residual ensemble, and shows a policy reducing cold-wave failure probability from 0.759 to 0.112.
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Low Latency Stand Alone Compute-Efficient Forecasting of Marine Engine Time Series Data
An adaptive multi-particle SDE framework using Euler-Maruyama discretization and Girsanov change of measure is developed for non-linear forecasting of marine engine parameters, claiming superior multi-step stability and efficiency over VARIMA.