Saved in:
| Main Authors: | Li, Chenyang, Sharma, Himanshu, Wu, Youcai, Magallanes, Joseph, Ramesh, K. T., Shields, Michael D. |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.03367 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Physics-constrained Gaussian Processes for Predicting Shockwave Hugoniot Curves
by: Pasparakis, George D., et al.
Published: (2026)
by: Pasparakis, George D., et al.
Published: (2026)
Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification
by: Sharma, Himanshu, et al.
Published: (2024)
by: Sharma, Himanshu, et al.
Published: (2024)
Peridynamic Neural Operators: A Data-Driven Nonlocal Constitutive Model for Complex Material Responses
by: Jafarzadeh, Siavash, et al.
Published: (2024)
by: Jafarzadeh, Siavash, et al.
Published: (2024)
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)
COPA/POE‐g‐MAH/Nano‐SiO 2 Composites for Fused Deposition Modeling: Preparation, Properties and Printing Parameter Optimization
by: Yajie Zeng, et al.
Published: (2025)
by: Yajie Zeng, et al.
Published: (2025)
Automated Constitutive Model Discovery by Pairing Sparse Regression Algorithms with Model Selection Criteria
by: Urrea-Quintero, Jorge-Humberto, et al.
Published: (2025)
by: Urrea-Quintero, Jorge-Humberto, et al.
Published: (2025)
Data-Driven Modeling of Dislocation Mobility from Atomistics using Physics-Informed Machine Learning
by: Tian, Yifeng, et al.
Published: (2024)
by: Tian, Yifeng, et al.
Published: (2024)
Polynomial Chaos Expansion for Operator Learning
by: Sharma, Himanshu, et al.
Published: (2025)
by: Sharma, Himanshu, et al.
Published: (2025)
Physics-Informed Gaussian Process Classification for Constraint-Aware Alloy Design
by: Hardcastle, Christofer, et al.
Published: (2025)
by: Hardcastle, Christofer, et al.
Published: (2025)
Physics-informed Polynomial Chaos Expansion with Enhanced Constrained Optimization Solver and D-optimal Sampling
by: Lu, Qitian, et al.
Published: (2025)
by: Lu, Qitian, et al.
Published: (2025)
Systematic Review of the Improvement of the Mechanical Properties of Concrete With Agave Fibers and Biochar
by: Yazmin O. Linares González, et al.
Published: (2026)
by: Yazmin O. Linares González, et al.
Published: (2026)
Incorporation of Physics‐Based Strengthening Coefficients into Phenomenological Crystal Plasticity Models
by: Nikhil Prabhu, et al.
Published: (2026)
by: Nikhil Prabhu, et al.
Published: (2026)
A REVIEW OF MACHINE-LEARNING SYNERGY IN HIGH-ENTROPY ALLOYS
by: Sharma, Himanshu, et al.
Published: (2025)
by: Sharma, Himanshu, et al.
Published: (2025)
AREVIEWOFMACHINE-LEARNINGSYNERGYINHIGH-ENTROPYALLOYS
by: Sharma, Himanshu, et al.
Published: (2025)
by: Sharma, Himanshu, et al.
Published: (2025)
Identifying Constitutive Parameters for Complex Hyperelastic Materials using Physics-Informed Neural Networks
by: Song, Siyuan, et al.
Published: (2023)
by: Song, Siyuan, et al.
Published: (2023)
Creep‐Fatigue Life Prediction of 316H Stainless Steel through Physics‐Informed Data‐Driven Models
by: Lianyong Xu, et al.
Published: (2025)
by: Lianyong Xu, et al.
Published: (2025)
Phenomenological and Physically Based Modeling of Flow Stress and Strengthening Mechanism of Inconel 718
by: Soheil Rooein, et al.
Published: (2026)
by: Soheil Rooein, et al.
Published: (2026)
Identification of Empirical Constitutive Models for Age-Hardenable Aluminium Alloy and High-Chromium Martensitic Steel Using Symbolic Regression
by: Kabliman, Evgeniya, et al.
Published: (2025)
by: Kabliman, Evgeniya, et al.
Published: (2025)
Influence of Ni Doping on the Structural, Morphological, Optical, and Electrical Properties of Nanocrystalline Cd1-xMnxS Thin Films
by: Pathok, Himanshu Sharma, et al.
Published: (2026)
by: Pathok, Himanshu Sharma, et al.
Published: (2026)
Symmetry- and Gradient-enhanced Gaussian Process Regression for the Active Learning of Potential Energy Surfaces in Porous Materials
by: Krondorfer, Johannes K., et al.
Published: (2025)
by: Krondorfer, Johannes K., et al.
Published: (2025)
The Fifteenth Britannica: A Thirty-Volume University?
by: Shields, Gerald
Published: (1974)
by: Shields, Gerald
Published: (1974)
Gaussian Process Regression-based Knowledge Distillation Framework for Simultaneous Prediction of Physical and Mechanical Properties of Epoxy Polymers
by: S., Sindu B., et al.
Published: (2026)
by: S., Sindu B., et al.
Published: (2026)
Phenomenology of altermagnets
by: Mostovoy, Maxim
Published: (2025)
by: Mostovoy, Maxim
Published: (2025)
Creep Constitutive Model of Rock Considering Gradient Damage
by: Shuguang Zhang, et al.
Published: (2026)
by: Shuguang Zhang, et al.
Published: (2026)
Data‐Driven Estimation of Fatigue Parameters in Concrete: A Minimal‐Input Approach Based on Compressive Strength
by: René Panian, et al.
Published: (2026)
by: René Panian, et al.
Published: (2026)
Extrapolative ML Models for Copolymers
by: Hashmi, Israrul H., et al.
Published: (2024)
by: Hashmi, Israrul H., et al.
Published: (2024)
Reproduction package for "Incorporation of Physics-based Strengthening Coefficients into Phenomenological Crystal Plasticity Models"
by: Prabhu, Nikhil, et al.
Published: (2025)
by: Prabhu, Nikhil, et al.
Published: (2025)
Bayesian neural networks for predicting uncertainty in full-field material response
by: Pasparakis, George D., et al.
Published: (2024)
by: Pasparakis, George D., et al.
Published: (2024)
Data Driven Calibration of Analytical Concrete Creep Models Considering Preloading Effects Using Gaussian Processes
by: Heller, Leonie, et al.
Published: (2026)
by: Heller, Leonie, et al.
Published: (2026)
Accelerating Surface Composition Characterization of Thin-Film Materials Libraries using Multi-Output Gaussian Process Regression
by: Thelen, F., et al.
Published: (2025)
by: Thelen, F., et al.
Published: (2025)
Cu$_2$ZnSiTe$_4$: A potential thermoelectric material with promising electronic transport
by: Sharma, Himanshu, et al.
Published: (2024)
by: Sharma, Himanshu, et al.
Published: (2024)
From Data-Driven Models to Physical Insight: Vibrational Entropy Governed by Atomic Volume
by: Tripathi, Shivam, et al.
Published: (2026)
by: Tripathi, Shivam, et al.
Published: (2026)
Learning thermodynamically constrained equations of state with uncertainty
by: Sharma, Himanshu, et al.
Published: (2023)
by: Sharma, Himanshu, et al.
Published: (2023)
A Constitutive Model for the Non‐shock Ignition and Mechanical Response of PBX Under Low Velocity Impact
by: Qin Fu, et al.
Published: (2025)
by: Qin Fu, et al.
Published: (2025)
Study on Deformation Behavior and Constitutive Models of Tungsten Alloys Under Electroplastic Effects
by: Mengpan Hu, et al.
Published: (2026)
by: Mengpan Hu, et al.
Published: (2026)
Discovery of Hyperelastic Constitutive Laws from Experimental Data with EUCLID
by: Abbasi, Arefeh, et al.
Published: (2025)
by: Abbasi, Arefeh, et al.
Published: (2025)
Laser‐Based Solidification of Cermets/Cemented Carbides: Processing‐Microstructure‐Property Relationships
by: Himanshu Singh Maurya, et al.
Published: (2025)
by: Himanshu Singh Maurya, et al.
Published: (2025)
A Physical‐Based Constitutive Modeling Coupled With Dynamic Spheroidization and Phase Transformation for Bimodal Ti–6Al–4V Alloy Within α+β Region
by: Hui‐Jie Zhang, et al.
Published: (2026)
by: Hui‐Jie Zhang, et al.
Published: (2026)
An Anisotropic Constitutive Relationship by a Series of 8 Chain Models
by: Yang, Libin, et al.
Published: (2024)
by: Yang, Libin, et al.
Published: (2024)
Precisely Engineering of Ångström‐Scale Dual Single Atom Drive [Co‐O] Spin‐Orbit Coupling to Boost Lithium–Oxygen Batteries Electrocatalysis
by: Yaning Fu, et al.
Published: (2024)
by: Yaning Fu, et al.
Published: (2024)
Similar Items
-
Physics-constrained Gaussian Processes for Predicting Shockwave Hugoniot Curves
by: Pasparakis, George D., et al.
Published: (2026) -
Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification
by: Sharma, Himanshu, et al.
Published: (2024) -
Peridynamic Neural Operators: A Data-Driven Nonlocal Constitutive Model for Complex Material Responses
by: Jafarzadeh, Siavash, et al.
Published: (2024) -
Constitutive Kolmogorov-Arnold Networks (CKANs): Combining Accuracy and Interpretability in Data-Driven Material Modeling
by: Abdolazizi, Kian P., et al.
Published: (2025) -
COPA/POE‐g‐MAH/Nano‐SiO 2 Composites for Fused Deposition Modeling: Preparation, Properties and Printing Parameter Optimization
by: Yajie Zeng, et al.
Published: (2025)