Cover Image

Adaptive Physics-Guided Neural Network

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Shulman, David, Dattner, Itai
Format:	Preprint
Published:	2024
Subjects:	Methodology Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Physics-Guided Inverse Regression for Crop Quality Assessment
by: Shulman, David, et al.
Published: (2024)

Robust Local Polynomial Regression with Similarity Kernels
by: Shulman, Yaniv
Published: (2025)

A nonparametric approach to understand multivariate quantile dynamics in financial time series
by: Rai, Kunal, et al.
Published: (2026)

Neural Backward Filtering Forward Guiding
by: Yang, Gefan, et al.
Published: (2026)

Geometry Adaptive Counterfactual Distribution Learning with Diffusion-Guided Smoothing
by: Kim, Kwangho
Published: (2026)

Bagged Polynomial Regression and Neural Networks
by: Klosin, Sylvia, et al.
Published: (2022)

Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks
by: Sainsbury-Dale, Matthew, et al.
Published: (2023)

Factor Augmented Tensor-on-Tensor Neural Networks
by: Zhou, Guanhao, et al.
Published: (2024)

Training of Spiking Neural Networks with Expectation-Propagation
by: Yao, Dan, et al.
Published: (2025)

Causal Estimation of Exposure Shifts with Neural Networks
by: Tec, Mauricio, et al.
Published: (2023)

A Locally Adaptive Algorithm for Multiple Testing with Network Structure
by: Liang, Ziyi, et al.
Published: (2022)

MOSAIC: Minimax-Optimal Sparsity-Adaptive Inference for Change Points in Dynamic Networks
by: Fan, Yingying, et al.
Published: (2025)

Robust Variable Selection and Estimation Via Adaptive Elastic Net S-Estimators for Linear Regression
by: Kepplinger, David
Published: (2021)

Doubly Robust Conditional Independence Testing with Generative Neural Networks
by: Zhang, Yi, et al.
Published: (2024)

Calibration Prediction Interval for Non-parametric Regression and Neural Networks
by: Wu, Kejin, et al.
Published: (2025)

Structure Maintained Representation Learning Neural Network for Causal Inference
by: Sun, Yang, et al.
Published: (2025)

Minimal Sufficient Representations for Self-interpretable Deep Neural Networks
by: Tan, Zhiyao, et al.
Published: (2026)

The Contextual Lasso: Sparse Linear Models via Deep Neural Networks
by: Thompson, Ryan, et al.
Published: (2023)

Integrating Causal Inference with Graph Neural Networks for Alzheimer's Disease Analysis
by: Peddi, Pranay Kumar, et al.
Published: (2025)

Constructive Universal Approximation and Sure Convergence for Multi-Layer Neural Networks
by: Chi, Chien-Ming
Published: (2025)

Covariate-dependent Graphical Model Estimation via Neural Networks with Statistical Guarantees
by: Lin, Jiahe, et al.
Published: (2025)

Integral Probability Metrics Meet Neural Networks: The Radon-Kolmogorov-Smirnov Test
by: Paik, Seunghoon, et al.
Published: (2023)

Self-Consistent Equation-guided Neural Networks for Censored Time-to-Event Data
by: Kim, Sehwan, et al.
Published: (2025)

ROC Analysis with Covariate Adjustment Using Neural Network Models: Evaluating the Role of Age in the Physical Activity-Mortality Association
by: Hammouri, Ziad Akram Ali, et al.
Published: (2025)

Adaptive Nonparametric Perturbations of Parametric Models with Generalized Bayes
by: Wu, Bohan, et al.
Published: (2024)

Learning Continuous Network Emerging Dynamics from Scarce Observations via Data-Adaptive Stochastic Processes
by: Cui, Jiaxu, et al.
Published: (2023)

Neural Networks with Causal Graph Constraints: A New Approach for Treatment Effects Estimation
by: Pros, Roger, et al.
Published: (2024)

Contrasting Global and Patient-Specific Regression Models via a Neural Network Representation
by: Behrens, Max, et al.
Published: (2026)

Multidimensional Distributional Neural Network Output Demonstrated in Super-Resolution of Surface Wind Speed
by: Goldwyn, Harrison J., et al.
Published: (2025)

Screening for Diabetes Mellitus in the U.S. Population Using Neural Network Models and Complex Survey Designs
by: Matabuena, Marcos, et al.
Published: (2024)

Understanding the Trade-offs in Accuracy and Uncertainty Quantification: Architecture and Inference Choices in Bayesian Neural Networks
by: Sheinkman, Alisa, et al.
Published: (2025)

DFNN: A Deep Fréchet Neural Network Framework for Learning Metric-Space-Valued Responses
by: Kim, Kyum, et al.
Published: (2025)

Probabilistic Graphical Model using Graph Neural Networks for Bayesian Inversion of Discrete Structural Component States
by: Li, Teng, et al.
Published: (2026)

Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text Generation
by: Arias, Esteban Garces, et al.
Published: (2024)

Efficient Adaptive Experimentation with Noncompliance
by: Oprescu, Miruna, et al.
Published: (2025)

Neural Networks Decoded: Targeted and Robust Analysis of Neural Network Decisions via Causal Explanations and Reasoning
by: Diallo, Alec F., et al.
Published: (2024)

Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference
by: Kelly, Ryan P., et al.
Published: (2023)

neuralGAM: An R Package for Fitting Generalized Additive Neural Networks
by: Ortega-Fernandez, Ines, et al.
Published: (2025)

Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood Risk
by: Pasche, Olivier C., et al.
Published: (2022)

DiffKnock: Diffusion-based Knockoff Statistics for Neural Networks Inference
by: Ge, Heng, et al.
Published: (2025)