Diversity-Aware Batch-Mode Active Learning for Efficient Sampling in Data-Driven Constitutive Modeling
Fuente:
arXiv
Saved in:
| Main Authors: | Shoghi, Ronak, Morand, Lukas, Helm, Dirk, Hartmaier, Alexander |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
A Workflow-Centric Approach to Generating FAIR Data Objects for Computationally Generated Microstructure-Sensitive Mechanical Data
by: Shoghi, Ronak, et al.
Published: (2024)
by: Shoghi, Ronak, et al.
Published: (2024)
A Workflow‐Centric Approach to Generating FAIR Data Objects for Computationally Generated Microstructure‐Sensitive Mechanical Data
by: Ronak Shoghi, et al.
Published: (2024)
by: Ronak Shoghi, et al.
Published: (2024)
Particle Trajectory Prediction in Discrete Element Simulations using a Graph-Based Interaction-Aware Model
by: Setty, Abhishek, et al.
Published: (2025)
by: Setty, Abhishek, et al.
Published: (2025)
Data-Driven Approach to Learning Optimal Forms of Constitutive Relations in Models Describing Lithium Plating in Battery Cells
by: Ahmadi, Avesta, et al.
Published: (2024)
by: Ahmadi, Avesta, et al.
Published: (2024)
Machine learning-based sampling of virtual experiments within the full stress state
by: Wessel, Alexander, et al.
Published: (2022)
by: Wessel, Alexander, et al.
Published: (2022)
Augmenting Simulated Noisy Quantum Data Collection by Orders of Magnitude Using Pre-Trajectory Sampling with Batched Execution
by: Patti, Taylor L., et al.
Published: (2025)
by: Patti, Taylor L., et al.
Published: (2025)
Multi-fidelity Batch Active Learning for Gaussian Process Classifiers
by: Cutforth, Murray, et al.
Published: (2025)
by: Cutforth, Murray, et al.
Published: (2025)
Constitutive Kolmogorov-Arnold Networks (CKANs): Combining Accuracy and Interpretability in Data-Driven Material Modeling
by: Abdolazizi, Kian P., et al.
Published: (2025)
by: Abdolazizi, Kian P., et al.
Published: (2025)
Subspace-Distance-Enabled Active Learning for Efficient Data-Driven Model Reduction of Parametric Dynamical Systems
by: Kapadia, Harshit, et al.
Published: (2025)
by: Kapadia, Harshit, et al.
Published: (2025)
Constitutive Models for Active Skeletal Muscle: Review, Comparison, and Application in a Novel Continuum Shoulder Model
by: Engelhardt, Laura, et al.
Published: (2024)
by: Engelhardt, Laura, et al.
Published: (2024)
Learning Optimal Forms of Constitutive Relations Characterizing Ion Intercalation from Data in Mathematical Models of Lithium-ion Batteries
by: Daniels, Lindsey, et al.
Published: (2023)
by: Daniels, Lindsey, et al.
Published: (2023)
Machine Learning and Data-Driven Methods in Computational Surface and Interface Science
by: Hörmann, Lukas, et al.
Published: (2025)
by: Hörmann, Lukas, et al.
Published: (2025)
Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties
by: Riccius, Leon, et al.
Published: (2024)
by: Riccius, Leon, et al.
Published: (2024)
Quantum Statistical Witness Indistinguishability
by: Nassar, Shafik, et al.
Published: (2025)
by: Nassar, Shafik, et al.
Published: (2025)
Possible ambient kinematics
by: Morand, Kevin
Published: (2023)
by: Morand, Kevin
Published: (2023)
Discovering Reaction Mechanisms with Transition Path Sampling-Based Active Learning of Machine-Learned Potentials
by: Lal, Ashique, et al.
Published: (2026)
by: Lal, Ashique, et al.
Published: (2026)
Physics-Informed Neural Network-Based Discovery of Hyperelastic Constitutive Models from Extremely Scarce Data
by: Moon, Hyeonbin, et al.
Published: (2025)
by: Moon, Hyeonbin, et al.
Published: (2025)
Language Model-Driven Data Pruning Enables Efficient Active Learning
by: Azeemi, Abdul Hameed, et al.
Published: (2024)
by: Azeemi, Abdul Hameed, et al.
Published: (2024)
Conformal Quantile Regression for Neural Probabilistic Constitutive Modeling
by: Bahmani, Bahador
Published: (2026)
by: Bahmani, Bahador
Published: (2026)
Characteristic Mode Analysis of Acoustic Scatterers
by: Jelinek, Lukas, et al.
Published: (2024)
by: Jelinek, Lukas, et al.
Published: (2024)
Discovery of Hyperelastic Constitutive Laws from Experimental Data with EUCLID
by: Abbasi, Arefeh, et al.
Published: (2025)
by: Abbasi, Arefeh, et al.
Published: (2025)
Distribution Learning for Molecular Regression
by: Shoghi, Nima, et al.
Published: (2024)
by: Shoghi, Nima, et al.
Published: (2024)
Characteristic Modes of Nonreciprocal Structures
by: Wingren, Niklas, et al.
Published: (2023)
by: Wingren, Niklas, et al.
Published: (2023)
Constitutive Priors for Inverse Design
by: Han, Jinkyo, et al.
Published: (2026)
by: Han, Jinkyo, et al.
Published: (2026)
Machine learning for structure-guided materials and process design
by: Morand, Lukas, et al.
Published: (2023)
by: Morand, Lukas, et al.
Published: (2023)
Data Efficient Contrastive Learning in Histopathology using Active Sampling
by: Reasat, Tahsin, et al.
Published: (2023)
by: Reasat, Tahsin, et al.
Published: (2023)
Effective Data-Driven Collective Variables for Free Energy Calculations from Metadynamics of Paths
by: Müllender, Lukas, et al.
Published: (2023)
by: Müllender, Lukas, et al.
Published: (2023)
Applying Machine Learning Methods to Laser Acceleration of Protons: Lessons Learned from Synthetic Data
by: Desai, Ronak, et al.
Published: (2023)
by: Desai, Ronak, et al.
Published: (2023)
Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials
by: Chen, Weilong, et al.
Published: (2025)
by: Chen, Weilong, et al.
Published: (2025)
Data-Efficient Neural Operator Training via Physics-Based Active Learning
by: Polanska, Alicja, et al.
Published: (2026)
by: Polanska, Alicja, et al.
Published: (2026)
Batch Bayesian Active Learning with Partial Batch Label Sampling
by: Hu, Kangping, et al.
Published: (2025)
by: Hu, Kangping, et al.
Published: (2025)
Efficient Forward-Mode Algorithmic Derivatives of Geant4
by: Aehle, Max, et al.
Published: (2024)
by: Aehle, Max, et al.
Published: (2024)
Quantum Search with In-Place Queries
by: Holman, Blake, et al.
Published: (2025)
by: Holman, Blake, et al.
Published: (2025)
Quantum Learning Theory Beyond Batch Binary Classification
by: Mohan, Preetham, et al.
Published: (2023)
by: Mohan, Preetham, et al.
Published: (2023)
Balancing Uncertainty and Diversity of Samples: Leveraging Diversity of Least, High Confidence Samples for Effective Active Learning
by: Arya, Vipul, et al.
Published: (2026)
by: Arya, Vipul, et al.
Published: (2026)
Computationally Efficient Nanophotonic Design through Data-Driven Eigenmode Expansion
by: Oktay, Mehmet Can, et al.
Published: (2024)
by: Oktay, Mehmet Can, et al.
Published: (2024)
ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints
by: Morato, Pablo G., et al.
Published: (2025)
by: Morato, Pablo G., et al.
Published: (2025)
Acoustics-based Active Control of Unsteady Flow Dynamics using Reinforcement Learning Driven Synthetic Jets
by: Rout, Siddharth, et al.
Published: (2023)
by: Rout, Siddharth, et al.
Published: (2023)
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU)
by: Tan, Jingye, et al.
Published: (2026)
by: Tan, Jingye, et al.
Published: (2026)
Evaluating GPT-3.5's Awareness and Summarization Abilities for European Constitutional Texts with Shared Topics
by: Greco, Candida M., et al.
Published: (2024)
by: Greco, Candida M., et al.
Published: (2024)
Similar Items
-
A Workflow-Centric Approach to Generating FAIR Data Objects for Computationally Generated Microstructure-Sensitive Mechanical Data
by: Shoghi, Ronak, et al.
Published: (2024) -
A Workflow‐Centric Approach to Generating FAIR Data Objects for Computationally Generated Microstructure‐Sensitive Mechanical Data
by: Ronak Shoghi, et al.
Published: (2024) -
Particle Trajectory Prediction in Discrete Element Simulations using a Graph-Based Interaction-Aware Model
by: Setty, Abhishek, et al.
Published: (2025) -
Data-Driven Approach to Learning Optimal Forms of Constitutive Relations in Models Describing Lithium Plating in Battery Cells
by: Ahmadi, Avesta, et al.
Published: (2024) -
Machine learning-based sampling of virtual experiments within the full stress state
by: Wessel, Alexander, et al.
Published: (2022)