Daniel Seidl

Sandia National Laboratories California — United States

Decision Sciences · Social Sciences

16h-index1.1kcitations121works1.32yr avg
OpenAlex↗ORCID↗Google Scholar↗Lab page↗

Accepting Students?

No reports yet. Know if this professor is taking students?

Research Topics

Probabilistic and Robust Engineering Design(23), Elasticity and Material Modeling(22), Advanced Multi-Objective Optimization Algorithms(17), Manufacturing Process and Optimization(11), Simulation Techniques and Applications(9)

Publications121 total

A physics-augmented neural network framework for modeling and detecting thermo-visco-plastic behavior
arXiv (Cornell University)·2025Open Access
Physics augmented machine learning discovery of composition-dependent constitutive laws for 3D printed digital materials
International Journal of Engineering Science·2025· 2 cited
A note on the reliability of goal-oriented error estimates for Galerkin finite element methods with nonlinear functionals
Applied Mathematics Letters·2025Open Access
Advancements in Constitutive Model Calibration: Leveraging the Power of Full‐Field DIC Measurements and In Situ Load Path Selection for Reliable Parameter Inference
Strain·2025
A General, Automated Method for Building Structural Tensors of Arbitrary Order for Anisotropic Function Representations
arXiv (Cornell University)·2025Open Access
Physics Augmented Machine Learning Discovery of Composition-Dependent Constitutive Laws for 3D Printed Digital Materials
ArXiv.org·2025Open Access
A comparative study of calibration techniques for finite strain elastoplasticity: Numerically-exact sensitivities for FEMU and VFM
Computer Methods in Applied Mechanics and Engineering·2025· 4 cited
Identification Uncertainty in Inverse Material Model Parameter Determination: A Sensitivity‐Based Decision Process for Load Path Selection
Strain·2025· 1 citedOpen Access
A direct-adjoint approach for material point model calibration with application to plasticity
Computational Materials Science·2025Open Access
An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass change
Earth System Dynamics·2025Open Access
Input Specific Neural Networks
arXiv (Cornell University)·2025Open Access
A Direct-adjoint Approach for Material Point Model Calibration with Application to Plasticity
arXiv (Cornell University)·2025Open Access
Interlaced Characterization and Calibration (ICC) for Improved Computational Simulation Credibility
2024· 1 citedOpen Access
Advancements in Constitutive Model Calibration: Leveraging the Power of Full-Field DIC Measurements and In-Situ Load Path Selection for Reliable Parameter Inference
arXiv (Cornell University)·2024Open Access
Digital image correlation and infrared thermography data for seven unique geometries of 304L stainless steel
Scientific Data·2024· 4 citedOpen Access
Calibration of Hybrid Constitutive Models from Full-field Data
2024Open Access
Optimizing Variational Quantum Circuits Using Metaheuristic Strategies in Reinforcement Learning
2024· 2 cited
Polyconvex neural network models of thermoelasticity
Journal of the Mechanics and Physics of Solids·2024· 17 citedOpen Access
Optimizing Variational Quantum Circuits Using Metaheuristic Strategies in Reinforcement Learning
arXiv (Cornell University)·2024Open Access
An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass-change
2024Open Access
page 1 of 7Next →

Frequent Co-authors

Michael Eldred(14), John Jakeman(11), Keith Dalbey(10), Kathryn Maupin(10), Jason Monschke(10), Laura Swiler(9), Gianluca Geraci(9), Friedrich Menhorn(7), Mohamed S. Ebeida(6), Xiaoshu Zeng(6), Russell Hooper(5), Patricia Hough(5), Mohammad Khalil(5), Elliott Ridgway(5), Justin Winokur(5), Samuel Fayad(5), Anh Tran(5), Brian Granzow(5), William Bohnhoff(4), Kenneth T. Hu(4)