{"total":164,"rows":[{"id":"2309.15325","title":"Neural Operators for Accelerating Scientific Simulations and Design","created":"2023-09-27","cited_by_count":221,"area":null},{"id":"2209.03302","title":"Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures?","created":"2022-09-07","cited_by_count":125,"area":null},{"id":"2406.02917","title":"A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks","created":"2024-06-05","cited_by_count":104,"area":null},{"id":"2211.08064","title":"Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications","created":"2022-11-15","cited_by_count":92,"area":null},{"id":"2302.13425","title":"A Survey on Uncertainty Quantification Methods for Deep Learning","created":"2023-02-26","cited_by_count":81,"area":null},{"id":"2303.10462","title":"Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review","created":"2023-03-18","cited_by_count":75,"area":null},{"id":"2410.13228","title":"From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning","created":"2024-10-17","cited_by_count":73,"area":null},{"id":"2212.06347","title":"Reliable extrapolation of deep neural operators informed by physics or sparse observations","created":"2022-12-13","cited_by_count":62,"area":null},{"id":"2203.03048","title":"Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors","created":"2022-03-06","cited_by_count":39,"area":null},{"id":"2306.05724","title":"Explaining Predictive Uncertainty with Information Theoretic Shapley Values","created":"2023-06-09","cited_by_count":36,"area":null},{"id":"2208.11866","title":"NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators","created":"2022-08-25","cited_by_count":33,"area":null},{"id":"2310.10776","title":"Correcting model misspecification in physics-informed neural networks (PINNs)","created":"2023-10-16","cited_by_count":33,"area":null},{"id":"2204.02488","title":"Discovering and forecasting extreme events via active learning in neural operators","created":"2022-04-05","cited_by_count":32,"area":null},{"id":"2301.02152","title":"L-HYDRA: Multi-Head Physics-Informed Neural Networks","created":"2023-01-05","cited_by_count":29,"area":null},{"id":"2304.11431","title":"A Review of Deep Learning for Video Captioning","created":"2023-04-22","cited_by_count":28,"area":null},{"id":"2311.11262","title":"Uncertainty quantification for noisy inputs-outputs in physics-informed neural networks and neural operators","created":"2023-11-19","cited_by_count":28,"area":null},{"id":"2402.15406","title":"Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks","created":"2024-02-23","cited_by_count":28,"area":null},{"id":"2305.04933","title":"Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial","created":"2023-05-07","cited_by_count":27,"area":null},{"id":"2408.01536","title":"Active Learning for Neural PDE Solvers","created":"2024-08-02","cited_by_count":22,"area":null},{"id":"2302.03271","title":"IB-UQ: Information bottleneck based uncertainty quantification for neural function regression and neural operator learning","created":"2023-02-07","cited_by_count":18,"area":null},{"id":"2310.18888","title":"D2NO: Efficient Handling of Heterogeneous Input Function Spaces with Distributed Deep Neural Operators","created":"2023-10-29","cited_by_count":18,"area":null},{"id":"2402.15115","title":"Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification","created":"2024-02-23","cited_by_count":17,"area":null},{"id":"2303.07647","title":"Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review","created":"2023-03-14","cited_by_count":15,"area":null},{"id":"2403.10642","title":"Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs","created":"2024-03-15","cited_by_count":15,"area":null},{"id":"2503.06320","title":"Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble","created":"2025-03-08","cited_by_count":13,"area":null},{"id":"2205.06948","title":"Bayesian Physics-Informed Extreme Learning Machine for Forward and Inverse PDE Problems with Noisy Data","created":"2022-05-14","cited_by_count":12,"area":null},{"id":"2403.04629","title":"Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration","created":"2024-03-07","cited_by_count":12,"area":null},{"id":"2502.09346","title":"Machine learning for modelling unstructured grid data in computational physics: a review","created":"2025-02-13","cited_by_count":12,"area":null},{"id":"2405.06443","title":"Residual-based Attention Physics-informed Neural Networks for Spatio-Temporal Ageing Assessment of Transformers Operated in Renewable Power Plants","created":"2024-05-10","cited_by_count":11,"area":null},{"id":"2408.09881","title":"Uncertainty Quantification of Surrogate Models using Conformal Prediction","created":"2024-08-19","cited_by_count":11,"area":null},{"id":"2306.00810","title":"Deep Operator Learning-based Surrogate Models with Uncertainty Quantification for Optimizing Internal Cooling Channel Rib Profiles","created":"2023-06-01","cited_by_count":10,"area":null},{"id":"2309.07946","title":"Slow Invariant Manifolds of Singularly Perturbed Systems via Physics-Informed Machine Learning","created":"2023-09-14","cited_by_count":10,"area":null},{"id":"2404.03099","title":"Composite Bayesian Optimization In Function Spaces Using NEON -- Neural Epistemic Operator Networks","created":"2024-04-03","cited_by_count":10,"area":null},{"id":"2206.14615","title":"Quantification of Deep Neural Network Prediction Uncertainties for VVUQ of Machine Learning Models","created":"2022-06-27","cited_by_count":9,"area":null},{"id":"2404.08809","title":"Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learning","created":"2024-04-12","cited_by_count":9,"area":null},{"id":"2406.12815","title":"Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation","created":"2024-06-18","cited_by_count":9,"area":null},{"id":"2505.23506","title":"Position: Epistemic uncertainty estimation methods are fundamentally incomplete","created":"2025-05-29","cited_by_count":9,"area":null},{"id":"2305.03184","title":"A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes","created":"2023-05-04","cited_by_count":8,"area":null},{"id":"2307.10438","title":"Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search","created":"2023-07-19","cited_by_count":8,"area":null},{"id":"2405.12380","title":"Fast meta-solvers for 3D complex-shape scatterers using neural operators trained on a non-scattering problem","created":"2024-05-20","cited_by_count":8,"area":null},{"id":"2502.04406","title":"Calibrated Physics-Informed Uncertainty Quantification","created":"2025-02-06","cited_by_count":8,"area":null},{"id":"2211.15498","title":"Physics-informed Neural Networks with Unknown Measurement Noise","created":"2022-11-28","cited_by_count":7,"area":null},{"id":"2303.05796","title":"Training, Architecture, and Prior for Deterministic Uncertainty Methods","created":"2023-03-10","cited_by_count":7,"area":null},{"id":"2407.04617","title":"Randomized Physics-Informed Neural Networks for Bayesian Data Assimilation","created":"2024-07-05","cited_by_count":7,"area":null},{"id":"2303.07392","title":"Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion","created":"2023-03-13","cited_by_count":6,"area":null},{"id":"2407.21217","title":"NeuroSEM: A hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements","created":"2024-07-30","cited_by_count":6,"area":null},{"id":"2508.00643","title":"Light-Weight Diffusion Multiplier and Uncertainty Quantification for Fourier Neural Operators","created":"2025-08-01","cited_by_count":6,"area":null},{"id":"2203.04515","title":"Structure and Distribution Metric for Quantifying the Quality of Uncertainty: Assessing Gaussian Processes, Deep Neural Nets, and Deep Neural Operators for Regression","created":"2022-03-09","cited_by_count":5,"area":null},{"id":"2302.09748","title":"Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles","created":"2023-02-20","cited_by_count":5,"area":null},{"id":"2303.12928","title":"Leveraging Multi-time Hamilton-Jacobi PDEs for Certain Scientific Machine Learning Problems","created":"2023-03-22","cited_by_count":5,"area":null},{"id":"2304.11925","title":"Data-driven modelling of brain activity using neural networks, Diffusion Maps, and the Koopman operator","created":"2023-04-24","cited_by_count":5,"area":null},{"id":"2312.17336","title":"PINN surrogate of Li-ion battery models for parameter inference. Part II: Regularization and application of the pseudo-2D model","created":"2023-12-28","cited_by_count":5,"area":null},{"id":"2401.00161","title":"DiffHybrid-UQ: Uncertainty Quantification for Differentiable Hybrid Neural Modeling","created":"2023-12-30","cited_by_count":5,"area":null},{"id":"2403.08901","title":"A Framework for Strategic Discovery of Credible Neural Network Surrogate Models under Uncertainty","created":"2024-03-13","cited_by_count":5,"area":null},{"id":"2404.12063","title":"FastVPINNs: Tensor-Driven Acceleration of VPINNs for Complex Geometries","created":"2024-04-18","cited_by_count":5,"area":null},{"id":"2406.14838","title":"Bayesian neural networks for predicting uncertainty in full-field material response","created":"2024-06-21","cited_by_count":5,"area":null},{"id":"2411.18425","title":"Streamlining Prediction in Bayesian Deep Learning","created":"2024-11-27","cited_by_count":5,"area":null},{"id":"2212.06965","title":"Error-Aware B-PINNs: Improving Uncertainty Quantification in Bayesian Physics-Informed Neural Networks","created":"2022-12-14","cited_by_count":4,"area":null},{"id":"2302.08406","title":"Entity Aware Modelling: A Survey","created":"2023-02-16","cited_by_count":4,"area":null},{"id":"2303.08884","title":"Discrete-Time Nonlinear Feedback Linearization via Physics-Informed Machine Learning","created":"2023-03-15","cited_by_count":4,"area":null},{"id":"2304.13950","title":"Fairness Uncertainty Quantification: How certain are you that the model is fair?","created":"2023-04-27","cited_by_count":4,"area":null},{"id":"2312.05153","title":"Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference","created":"2023-12-08","cited_by_count":4,"area":null},{"id":"2404.14642","title":"Uncertainty Quantification on Graph Learning: A Survey","created":"2024-04-23","cited_by_count":4,"area":null},{"id":"2408.00681","title":"Alpha-VI DeepONet: A prior-robust variational Bayesian approach for enhancing DeepONets with uncertainty quantification","created":"2024-08-01","cited_by_count":4,"area":null},{"id":"2410.01340","title":"Response Estimation and System Identification of Dynamical Systems via Physics-Informed Neural Networks","created":"2024-10-02","cited_by_count":4,"area":null},{"id":"2507.21273","title":"Deep Polynomial Chaos Expansion","created":"2025-07-28","cited_by_count":4,"area":null},{"id":"2604.17156","title":"Uncertainty Quantification in PINNs for Turbulent Flows: Bayesian Inference and Repulsive Ensembles","created":"2026-04-18","cited_by_count":4,"area":null},{"id":"2308.06447","title":"A Sequential Meta-Transfer (SMT) Learning to Combat Complexities of Physics-Informed Neural Networks: Application to Composites Autoclave Processing","created":"2023-08-12","cited_by_count":3,"area":null},{"id":"2311.07798","title":"Probabilistic Physics-integrated Neural Differentiable Modeling for Isothermal Chemical Vapor Infiltration Process","created":"2023-11-13","cited_by_count":3,"area":null},{"id":"2402.05939","title":"Uncertainty Awareness of Large Language Models Under Code Distribution Shifts: A Benchmark Study","created":"2024-01-12","cited_by_count":3,"area":null},{"id":"2404.00502","title":"Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation","created":"2024-03-31","cited_by_count":3,"area":null},{"id":"2406.08853","title":"Assessment of Uncertainty Quantification in Universal Differential Equations","created":"2024-06-13","cited_by_count":3,"area":null},{"id":"2407.00616","title":"DADEE: Well-calibrated uncertainty quantification in neural networks for barriers-based robot safety","created":"2024-06-30","cited_by_count":3,"area":null},{"id":"2408.07201","title":"Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology","created":"2024-08-13","cited_by_count":3,"area":null},{"id":"2409.06560","title":"A Primer on Variational Inference for Physics-Informed Deep Generative Modelling","created":"2024-09-10","cited_by_count":3,"area":null},{"id":"2501.01453","title":"Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries","created":"2024-12-31","cited_by_count":3,"area":null},{"id":"2503.21059","title":"Uncertainty propagation in feed-forward neural network models","created":"2025-03-27","cited_by_count":3,"area":null},{"id":"2504.15240","title":"Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning","created":"2025-04-21","cited_by_count":3,"area":null},{"id":"2505.06459","title":"Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundles","created":"2025-05-09","cited_by_count":3,"area":null},{"id":"2509.15933","title":"Bayesian Physics Informed Neural Networks for Reliable Transformer Prognostics","created":"2025-09-19","cited_by_count":3,"area":null},{"id":"2510.05054","title":"HybridFlow: Quantification of Aleatoric and Epistemic Uncertainty with a Single Hybrid Model","created":"2025-10-06","cited_by_count":3,"area":null},{"id":"2512.13746","title":"Probabilistic Predictions of Process-Induced Deformation in Carbon/Epoxy Composites Using a Deep Operator Network","created":"2025-12-15","cited_by_count":3,"area":null},{"id":"2209.01900","title":"A Robust Learning Methodology for Uncertainty-aware Scientific Machine Learning models","created":"2022-09-05","cited_by_count":2,"area":null},{"id":"2210.14287","title":"Combined Data and Deep Learning Model Uncertainties: An Application to the Measurement of Solid Fuel Regression Rate","created":"2022-10-25","cited_by_count":2,"area":null},{"id":"2212.00881","title":"Investigating Deep Learning Model Calibration for Classification Problems in Mechanics","created":"2022-12-01","cited_by_count":2,"area":null},{"id":"2303.08455","title":"On the uncertainty analysis of the data-enabled physics-informed neural network for solving neutron diffusion eigenvalue problem","created":"2023-03-15","cited_by_count":2,"area":null},{"id":"2306.15159","title":"Evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems","created":"2023-06-27","cited_by_count":2,"area":null},{"id":"2309.15128","title":"DPA-WNO: A gray box model for a class of stochastic mechanics problem","created":"2023-09-24","cited_by_count":2,"area":null},{"id":"2311.13812","title":"Mechanical Characterization and Inverse Design of Stochastic Architected Metamaterials Using Neural Operators","created":"2023-11-23","cited_by_count":2,"area":null},{"id":"2402.13945","title":"Probabilistic Neural Networks (PNNs) for Modeling Aleatoric Uncertainty in Scientific Machine Learning","created":"2024-02-21","cited_by_count":2,"area":null},{"id":"2403.11591","title":"A physics-informed neural network method for the approximation of slow invariant manifolds for the general class of stiff systems of ODEs","created":"2024-03-18","cited_by_count":2,"area":null},{"id":"2406.17812","title":"Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars","created":"2024-06-24","cited_by_count":2,"area":null},{"id":"2408.02581","title":"Operational range bounding of spectroscopy models with anomaly detection","created":"2024-08-05","cited_by_count":2,"area":null},{"id":"2409.00220","title":"Learning Latent Space Dynamics with Model-Form Uncertainties: A Stochastic Reduced-Order Modeling Approach","created":"2024-08-30","cited_by_count":2,"area":null},{"id":"2409.08666","title":"Towards certifiable AI in aviation: landscape, challenges, and opportunities","created":"2024-09-13","cited_by_count":2,"area":null},{"id":"2501.08501","title":"Scalable Bayesian Physics-Informed Kolmogorov-Arnold Networks","created":"2025-01-15","cited_by_count":2,"area":null},{"id":"2503.17385","title":"Uncertainty Quantification for Data-Driven Machine Learning Models in Nuclear Engineering Applications: Where We Are and What Do We Need?","created":"2025-03-16","cited_by_count":2,"area":null},{"id":"2503.22712","title":"Coverage-Guaranteed Speech Emotion Recognition via Calibrated Uncertainty-Adaptive Prediction Sets","created":"2025-03-24","cited_by_count":2,"area":null},{"id":"2504.19013","title":"$PINN - a Domain Decomposition Method for Bayesian Physics-Informed Neural Networks","created":"2025-04-26","cited_by_count":2,"area":null},{"id":"2512.01080","title":"Building Trustworthy AI for Materials Discovery: From Autonomous Laboratories to Z-scores","created":"2025-11-30","cited_by_count":2,"area":null}],"next_cursor":"100"}