22 citations · 47 across the 22 of their papers we have counts for
24 papers
Learning Gradient Flow: Using Equation Discovery to Accelerate Engineering Optimization
Grant Norman, Conor Rowan, Kurt Maute +1
In this work, we investigate the use of data-driven equation discovery for dynamical systems to model and forecast continuous-time dynamics of unconstrained optimization problems.…
Boundary condition enforcement with PINNs: a comparative study and verification on 3D geometries
Conor Rowan, Kai Hampleman, Kurt Maute +1
Since their advent nearly a decade ago, physics-informed neural networks (PINNs) have been studied extensively as a novel technique for solving forward and inverse problems in phys…
Solving engineering eigenvalue problems with neural networks using the Rayleigh quotient
Conor Rowan, John Evans, Kurt Maute +1
From characterizing the speed of a thermal system's response to computing natural modes of vibration, eigenvalue analysis is ubiquitous in engineering. In spite of this, eigenvalue…
Physics-informed solution reconstruction in elasticity and heat transfer using the explicit constraint force method
Conor Rowan, Kurt Maute, Alireza Doostan
One use case of ``physics-informed neural networks'' (PINNs) is solution reconstruction, which aims to estimate the full-field state of a physical system from sparse measurements.…
Enriched Immersed Finite Element and Isogeometric Analysis -- Algorithms and Data Structures
Nils Wunsch, Keenan Doble, Mathias R. Schmidt +3
Immersed finite element methods provide a convenient analysis framework for problems involving geometrically complex domains, such as those found in topology optimization and micro…
Constrained or Unconstrained? Neural-Network-Based Equation Discovery from Data
Grant Norman, Jacqueline Wentz, Hemanth Kolla +2
Throughout many fields, practitioners often rely on differential equations to model systems. Yet, for many applications, the theoretical derivation of such equations and/or accurat…
Interpolation-based immersogeometric analysis methods for multi-material and multi-physics problems
Jennifer E. Fromm, Nils Wunsch, Kurt Maute +2
Immersed boundary methods are high-order accurate computational tools used to model geometrically complex problems in computational mechanics. While traditional finite element meth…
Extended isogeometric analysis of multi-material and multi-physics problems using hierarchical B-splines
Mathias Schmidt, Lise Noel, Keenan Doble +2
This paper presents an immersed, isogeometric finite element framework to predict the response of multi-material, multi-physics problems with complex geometries using locally refin…
Interpolation-based immersed finite element and isogeometric analysis
Jennifer E. Fromm, Nils Wunsch, Ru Xiang +4
We introduce a new paradigm for immersed finite element and isogeometric methods based on interpolating function spaces from an unfitted background mesh into Lagrange finite elemen…
XIGA: An eXtended IsoGeometric Analysis approach for multi-material problems
L. Noel, M. Schmidt, K. Doble +2
Multi-material problems often exhibit complex geometries along with physical responses presenting large spatial gradients or discontinuities. In these cases, providing high-quality…
Automated processing of X-ray computed tomography images via panoptic segmentation for modeling woven composite textiles
Aaron Allred, Lauren J. Abbott, Alireza Doostan +1
A new, machine learning-based approach for automatically generating 3D digital geometries of woven composite textiles is proposed to overcome the limitations of existing analytical…
Topology Optimization under Microscale Uncertainty using Stochastic Gradients
Subhayan De, Kurt Maute, Alireza Doostan
This paper considers the design of structures made of engineered materials, accounting for uncertainty in material properties. We present a topology optimization approach that opti…
Minimum Feature Size Control in Level Set Topology Optimization via Density Fields
Jorge L. Barrera, Markus J. Geiss, Kurt Maute
A level set topology optimization approach that uses an auxiliary density field to nucleate holes during the optimization process and achieves minimum feature size control in optim…
Reliability-based Topology Optimization using Stochastic Gradients
Subhayan De, Kurt Maute, Alireza Doostan
This paper addresses the computational challenges in reliability-based topology optimization (RBTO) of structures associated with the estimation of statistics of the objective and…
Ambiguous phase assignment of discretized 3D geometries in topology optimization
Jorge L. Barrera, Kurt Maute
Level set-based immersed boundary techniques operate on nonconforming meshes while providing a crisp definition of interface and external boundaries. In such techniques, an isocont…
Bi-fidelity Stochastic Gradient Descent for Structural Optimization under Uncertainty
Subhayan De, Kurt Maute, Alireza Doostan
The presence of uncertainty in material properties and geometry of a structure is ubiquitous. The design of robust engineering structures, therefore, needs to incorporate uncertain…
Hole Seeding in Level Set Topology Optimization via Density Fields
Jorge L. Barrera, Markus J. Geiss, Kurt Maute
Two approaches that use a density field for seeding holes in level set topology optimization are proposed. In these approaches, the level set field describes the material-void inte…
Adaptive level set topology optimization using hierarchical B-splines
L. Noel, M. Schmidt, C. Messe +2
This paper presents an adaptive discretization strategy for level set topology optimization of structures based on hierarchical B-splines. This work focuses on the influence of the…
Topology Optimization under Uncertainty using a Stochastic Gradient-based Approach
Subhayan De, Jerrad Hampton, Kurt Maute +1
Topology optimization under uncertainty (TOuU) often defines objectives and constraints by statistical moments of geometric and physical quantities of interest. Most traditional TO…
CutFEM topology optimization of 3D laminar incompressible flow problems
Hernan Villanueva, Kurt Maute
This paper studies the characteristics and applicability of the CutFEM approach as the core of a robust topology optimization framework for 3D laminar incompressible flow and speci…
Level set shape and topology optimization of finite strain bilateral contact problems
Matthew Lawry, Kurt Maute
This paper presents a method for the optimization of multi-component structures comprised of two and three materials considering large motion sliding contact and separation along i…
On Uncertainty Quantification of Lithium-ion Batteries: Application to an LiC/LiCoO cell
Mohammad Hadigol, Kurt Maute, Alireza Doostan
In this work, a stochastic, physics-based model for Lithium-ion batteries (LIBs) is presented in order to study the effects of parametric model uncertainties on the cell capacity,…
Density and Level Set-XFEM Schemes for Topology Optimization of 3-D Structures
Carlos H. Villanueva, Kurt Maute
As the capabilities of additive manufacturing techniques increase, topology optimization provides a promising approach to design geometrically sophisticated structures which can be…
A Simple and Efficient Preconditioning Scheme for Heaviside Enriched XFEM
Christapher Lang, David Makhija, Alireza Doostan +1
The eXtended Finite Element Method (XFEM) is an approach for solving problems with non-smooth solutions. In the XFEM, the approximate solution is locally enriched to capture discon…