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Uncertainty Quantification

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Showing 1951–2000 of 2366 papers

TitleStatusHype
Physics-constrained Bayesian inference of state functions in classical density-functional theory—0
Learning particle swarming models from data with Gaussian processes—0
Data-Driven Gradient Optimization for Field Emission Management in a Superconducting Radio-Frequency Linac—0
Data-driven method for real-time prediction and uncertainty quantification of fatigue failure under stochastic loading using artificial neural networks and Gaussian process regression—0
Data-Driven Model Selections of Second-Order Particle Dynamics via Integrating Gaussian Processes with Low-Dimensional Interacting Structures—0
Data-driven multi-agent modelling of calcium interactions in cell culture: PINN vs Regularized Least-squares—0
Data-Driven Physics-Informed Neural Networks: A Digital Twin Perspective—0
Data-driven polynomial chaos expansion for machine learning regression—0
Data-Driven Prediction and Uncertainty Quantification of PWR Crud-Induced Power Shift Using Convolutional Neural Networks—0
Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models—0
Data-Driven Robust Covariance Control for Uncertain Linear Systems—0
Data-Driven Surrogate Modeling Techniques to Predict the Effective Contact Area of Rough Surface Contact Problems—0
Data-Driven Uncertainty-Aware Forecasting of Sea Ice Conditions in the Gulf of Ob Based on Satellite Radar Imagery—0
Data-driven Uncertainty Quantification in Computational Human Head Models—0
Harnessing On-Machine Metrology Data for Prints with a Surrogate Model for Laser Powder Directed Energy Deposition—0
Data-Efficient Task Generalization via Probabilistic Model-based Meta Reinforcement Learning—0
Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems—0
What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization—0
DAWN-FM: Data-Aware and Noise-Informed Flow Matching for Solving Inverse Problems—0
DBCal: Density Based Calibration of classifier predictions for uncertainty quantification—0
Debiasing Mini-Batch Quadratics for Applications in Deep Learning—0
Decision Theoretic Bootstrapping—0
Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents—0
Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics—0
Deep Capsule Encoder-Decoder Network for Surrogate Modeling and Uncertainty Quantification—0
Deep Compositional Spatial Models—0
Deep composition of tensor-trains using squared inverse Rosenblatt transports—0
Deep Convolutional Architectures for Extrapolative Forecast in Time-dependent Flow Problems—0
Deep convolutional neural networks for uncertainty propagation in random fields—0
Deep Deterministic Uncertainty: A New Simple Baseline—0
Deep Echo State Networks with Uncertainty Quantification for Spatio-Temporal Forecasting—0
Deep Ensemble as a Gaussian Process Approximate Posterior—0
Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecasting—0
Deep Ensembles from a Bayesian Perspective—0
Deep Ensembles to Improve Uncertainty Quantification of Statistical Downscaling Models under Climate Change Conditions—0
Deep Evidential Learning for Bayesian Quantile Regression—0
Deep Evidential Learning for Radiotherapy Dose Prediction—0
DeepGLEAM: A hybrid mechanistic and deep learning model for COVID-19 forecasting—0
Deep interpretable ensembles—0
Deep Latent Force Models: ODE-based Process Convolutions for Bayesian Deep Learning—0
Deep Learning Aided Laplace Based Bayesian Inference for Epidemiological Systems—0
Deep Learning: A Tutorial—0
Deep Learning-Based BMD Estimation from Radiographs with Conformal Uncertainty Quantification—0
Deep learning-based surrogate model for 3-D patient-specific computational fluid dynamics—0
Deep learning electromagnetic inversion with convolutional neural networks—0
Deep Learning for Ranking Response Surfaces with Applications to Optimal Stopping Problems—0
Screening for Diabetes Mellitus in the U.S. Population Using Neural Network Models and Complex Survey Designs—0
Deep Learning to advance the Eigenspace Perturbation Method for Turbulence Model Uncertainty Quantification—0
Deep neural network surrogates for non-smooth quantities of interest in shape uncertainty quantification—0
Deep Neural Operator Enabled Digital Twin Modeling for Additive Manufacturing—0
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