Daniel Seidl
YOU?
Author Swipe
View article: A note on the reliability of goal-oriented error estimates for Galerkin finite element methods with nonlinear functionals
A note on the reliability of goal-oriented error estimates for Galerkin finite element methods with nonlinear functionals Open
View article: A General, Automated Method for Building Structural Tensors of Arbitrary Order for Anisotropic Function Representations
A General, Automated Method for Building Structural Tensors of Arbitrary Order for Anisotropic Function Representations Open
We present a general, constructive procedure to find the basis for tensors of arbitrary order subject to linear constraints by transforming the problem to that of finding the nullspace of a linear operator. The proposed method utilizes sta…
View article: Physics Augmented Machine Learning Discovery of Composition-Dependent Constitutive Laws for 3D Printed Digital Materials
Physics Augmented Machine Learning Discovery of Composition-Dependent Constitutive Laws for 3D Printed Digital Materials Open
Multi-material 3D printing, particularly through polymer jetting, enables the fabrication of digital materials by mixing distinct photopolymers at the micron scale within a single build to create a composite with tunable mechanical propert…
View article: Identification Uncertainty in Inverse Material Model Parameter Determination: A Sensitivity‐Based Decision Process for Load Path Selection
Identification Uncertainty in Inverse Material Model Parameter Determination: A Sensitivity‐Based Decision Process for Load Path Selection Open
This research proposes a sensitivity‐based framework for selecting the optimal prescribed loading path for a biaxial cruciform specimen. Optimality here is determined by the direction and magnitude of the prescribed displacement that minim…
View article: A direct-adjoint approach for material point model calibration with application to plasticity
A direct-adjoint approach for material point model calibration with application to plasticity Open
View article: An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass change
An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass change Open
This study investigated the computational benefits of using multi-fidelity statistical estimation (MFSE) algorithms to quantify uncertainty in the mass change of Humboldt Glacier, Greenland, between 2007 and 2100 using a single climate cha…
View article: Input Specific Neural Networks
Input Specific Neural Networks Open
The black-box nature of neural networks limits the ability to encode or impose specific structural relationships between inputs and outputs. While various studies have introduced architectures that ensure the network's output adheres to a …
View article: A Direct-adjoint Approach for Material Point Model Calibration with Application to Plasticity
A Direct-adjoint Approach for Material Point Model Calibration with Application to Plasticity Open
This paper proposes a new approach for the calibration of material parameters in local elastoplastic constitutive models. The calibration is posed as a constrained optimization problem, where the constitutive model evolution equations for …
View article: Interlaced Characterization and Calibration (ICC) for Improved Computational Simulation Credibility
Interlaced Characterization and Calibration (ICC) for Improved Computational Simulation Credibility Open
Accurate material characterization and model calibration are pivotal for simulations used for high-consequence engineering decisions. Current characterization and calibration methods (1) use simplified test specimen geometries and global d…
View article: Advancements in Constitutive Model Calibration: Leveraging the Power of Full-Field DIC Measurements and In-Situ Load Path Selection for Reliable Parameter Inference
Advancements in Constitutive Model Calibration: Leveraging the Power of Full-Field DIC Measurements and In-Situ Load Path Selection for Reliable Parameter Inference Open
Accurate material characterization and model calibration are essential for computationally-supported engineering decisions. Current characterization and calibration methods (1) use simplified test specimen geometries and global data, (2) c…
View article: Digital image correlation and infrared thermography data for seven unique geometries of 304L stainless steel
Digital image correlation and infrared thermography data for seven unique geometries of 304L stainless steel Open
Material Testing 2.0 (MT2.0) is a paradigm that advocates for the use of rich, full-field data, such as from digital image correlation and infrared thermography, for material identification. By employing heterogeneous, multi-axial data in …
View article: Calibration of Hybrid Constitutive Models from Full-field Data
Calibration of Hybrid Constitutive Models from Full-field Data Open
View article: Polyconvex neural network models of thermoelasticity
Polyconvex neural network models of thermoelasticity Open
View article: Optimizing Variational Quantum Circuits Using Metaheuristic Strategies in Reinforcement Learning
Optimizing Variational Quantum Circuits Using Metaheuristic Strategies in Reinforcement Learning Open
Quantum Reinforcement Learning (QRL) offers potential advantages over classical Reinforcement Learning, such as compact state space representation and faster convergence in certain scenarios. However, practical benefits require further val…
View article: An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass-change
An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass-change Open
This study investigated the computational benefits of using multi-fidelity uncertainty quantification (MFUQ) algorithms to quantify uncertainty in the mass change of Humboldt Glacier, Greenland, between 2007 and 2100 using a single climate…
View article: Bayesian Optimal Experimental Design for Constitutive Model Calibration Using Full-Field DIC Data
Bayesian Optimal Experimental Design for Constitutive Model Calibration Using Full-Field DIC Data Open
View article: Calibration of Hybrid Constitutive Models from Full-field Data
Calibration of Hybrid Constitutive Models from Full-field Data Open
View article: Interlaced Material Characterization and Model Calibration (ICC) for Improved Computational Simulation
Interlaced Material Characterization and Model Calibration (ICC) for Improved Computational Simulation Open
View article: Calibration of Hybrid Constitutive Models from Full-field Data
Calibration of Hybrid Constitutive Models from Full-field Data Open
View article: Interlaced Characterization and Calibration: In-situ Bayesian optimal experimental design for constitutive model calibration
Interlaced Characterization and Calibration: In-situ Bayesian optimal experimental design for constitutive model calibration Open
View article: Polyconvex neural network models of thermoelasticity
Polyconvex neural network models of thermoelasticity Open
Machine-learning function representations such as neural networks have proven to be excellent constructs for constitutive modeling due to their flexibility to represent highly nonlinear data and their ability to incorporate constitutive co…
View article: Constitutive Models via Automatic Differentiation v.1.0.0
Constitutive Models via Automatic Differentiation v.1.0.0 Open
SAND2024-00905O Constitutive Models via Automatic Differentiation (CMAD) provides a software framework for solving constitutive or material model calibration problems. It relies on JAX's automatic differentiation capabilities to compute th…
View article: Polyconvex Neural Network Models of Thermoelasticity
Polyconvex Neural Network Models of Thermoelasticity Open
View article: MULTILEVEL MONTE CARLO ESTIMATORS FOR DERIVATIVE-FREE OPTIMIZATION UNDER UNCERTAINTY
MULTILEVEL MONTE CARLO ESTIMATORS FOR DERIVATIVE-FREE OPTIMIZATION UNDER UNCERTAINTY Open
Optimization is a key tool for scientific and engineering applications; however, in the presence of models affected by uncertainty, the optimization formulation needs to be extended to consider statistics of the quantity of interest. Optim…
View article: Bayesian optimal experimental design for constitutive model calibration
Bayesian optimal experimental design for constitutive model calibration Open
View article: Levelling of Finite Element Models for Material Model Calibration using Digital Image Correlation.
Levelling of Finite Element Models for Material Model Calibration using Digital Image Correlation. Open
View article: Linearization errors in discrete goal-oriented error estimation
Linearization errors in discrete goal-oriented error estimation Open
View article: Machine Learning Surrogates for Fuel Degradation Processes in Nuclear Waste Repository Simulations
Machine Learning Surrogates for Fuel Degradation Processes in Nuclear Waste Repository Simulations Open
View article: Linearization Errors in Discrete Goal-Oriented Error Estimation
Linearization Errors in Discrete Goal-Oriented Error Estimation Open
View article: A Goal-oriented Approach to Model Form Error for Constitutive Models.
A Goal-oriented Approach to Model Form Error for Constitutive Models. Open