Jiun-Shyan Chen

University of California, San Diego | UCSD · Department of Structural Engineering

A parametric adaptive greedy Latent Space Dynamics Identification (gLaSDI) framework is developed for accurate, efficient, and certified data-driven physics-informed greedy auto-encoder simulators of high-dimensional nonlinear dynamical systems. In the proposed framework, an auto-encoder and dynamics identification models are trained interactively...

Physics-constrained data-driven computing is an emerging computational paradigm that allows simulation of complex materials directly based on material database and bypass the classical constitutive model construction. However, it remains difficult to deal with high-dimensional applications and extrapolative generalization. This paper introduces dee...

The numerical modelling of natural disasters such as landslides presents several challenges for conventional mesh-based methods such as the finite element method (FEM) due to the presence of numerically challenging phenomena such as severe material deformation and fragmentation. In contrast, meshfree methods such as the reproducing kernel particle...

Identification of muscle-tendon force generation properties and muscle activities from physiological measurements, e.g., motion data and raw surface electromyography (sEMG), offers the opportunities for construction of a subject-specific musculoskeletal (MSK) digital twin system for health conditions assessment and human motion prediction. While ma...

Characterization and modeling of path-dependent behaviors of complex materials by phenomenological models remains challenging due to difficulties in formulating mathematical expressions and internal state variables (ISVs) governing path-dependent behaviors. Data-driven machine learning models, such as deep neural networks and recurrent neural netwo...

The numerical modelling of natural disasters such as landslides presents several challenges for conventional mesh-based methods such as the finite element method (FEM) due to the presence of numerically challenging phenomena such as severe material deformation and fragmentation. In contrast, meshfree methods such as the reproducing kernel particle...

Characterization and modeling of path-dependent behaviors of complex materials by phenomenological models remains challenging due to difficulties in formulating mathematical expressions and internal state variables (ISVs) governing path-dependent behaviors. Data-driven machine learning models, such as deep neural networks and recurrent neural netwo...

A parametric adaptive physics-informed greedy Latent Space Dynamics Identification (gLaSDI) method is proposed for accurate, efficient, and robust data-driven reduced-order modeling of high-dimensional nonlinear dynamical systems. In the proposed gLaSDI framework, an autoencoder discovers intrinsic nonlinear latent representations of high-dimension...

The cover image is based on the Basic Research Multiscale modeling of passive material influences on deformation and force output of skeletal muscles by Xiaolong He et al., https://doi.org/10.1002/cnm.3571.

We develop an immersed meshfree method under a variational multiscale framework for modeling fluid–structure interactive systems involving shock waves. The proposed method enables flexible non-body-fitted discretization, approximations, and quadrature rules for solid and fluid subdomains. The interfacial compatibility conditions are imposed by a vo...

Passive materials in human skeletal muscle tissues play an important role in force output of skeletal muscles. This paper introduces a multiscale modeling framework to investigate how age-associated variations in micro-scale passive muscle components, including microstructural geometry (e.g., connective tissue thickness) and material properties (e....

Physics-constrained data-driven computing is an emerging computational paradigm that allows simulation of complex materials directly based on material database and bypass the classical constitutive model construction. However, it remains difficult to deal with high-dimensional applications and extrapolative generalization. This paper introduces dee...

A level set topology optimization (LSTO) using the stabilized nodally integrated reproducing kernel particle method (RKPM) to solve the governing equations is introduced in this paper. This methodology allows for an exact geometry description of a structure at each iteration without remeshing and without any interpolation scheme. Moreover, useful c...

In the reproducing kernel particle method (RKPM), the approximation is achieved through construction of shape functions in the physical domain and the interaction of neighbouring nodes. When modelling large deformation problems, Lagrangian and semi-Lagrangian formulations have been proposed, where the RK functions are evaluated in the reference ini...

The explosive welding process is an extreme-deformation problem that involves shock waves, large plastic deformation, and fragmentation around the collision point, which are extremely challenging features to model for the traditional mesh-based methods. In this work, a particle-based Godunov shock algorithm under a semi-Lagrangian reproducing kerne...

We introduce an immersed meshfree formulation for modeling heterogeneous materials with flexible non-body-fitted discretizations, approximations, and quadrature rules. The interfacial compatibility condition is imposed by a volumetric constraint, which avoids a tedious contour integral for complex material geometry. The proposed immersed approach i...

Data-driven modeling directly utilizes experimental data with machine learning techniques to predict a material’s response without the necessity of using phenomenological constitutive models. Although data-driven modeling presents a promising new approach, it has yet to be extended to the modeling of large-deformation bio-tissues. Herein, we extend...

The focus of this review is the application of advanced MRI to study the effect of aging and disuse related remodeling of the extracellular matrix (ECM) on force transmission in the human musculoskeletal system. Structural MRI includes (i) ultra-low echo times (UTE) maps to visualize and quantify the connective tissue, (ii) diffusion tensor imaging...

Physics-constrained data-driven computing is an emerging hybrid approach that integrates universal physical laws with data-driven models of experimental data for scientific computing. A new data-driven simulation approach coupled with a locally convex reconstruction, termed the local convexity data-driven (LCDD) computing, is proposed to enhance ac...

This paper presents a level set topology optimization method in combination with the reproducing kernel particle method (RKPM) for the design of structures subjected to design-dependent pressure loads. RKPM allows for arbitrary particle placement in discretization and approximation of unknowns. This attractive property in combination with the impli...

A stabilized meshfree formulation for modeling nonlinear, multiphase porous media with application to landslide simulation is presented. To effectively capture the hydromechanical couplings between solid and fluid phases, an efficient equal-order approximation pair is adopted in conjunction with the fluid pressure projection in the mixed formulatio...

As characterization and modeling of complex materials by phenomenological models remains challenging, data-driven computing that performs physical simulations directly from material data has attracted considerable attention. Data-driven computing is a general computational mechanics framework that consists of a physical solver and a material solver...

The Waste Isolation Pilot Plant (WIPP) is a geologic repository for defense-related nuclear waste. If left undisturbed, the virtually impermeable rock salt surrounding the repository will isolate the nuclear waste from the biosphere. If humans accidentally intrude into the repository in the future, then the likelihood of a radionuclide release to t...

Human aging results in a progressive decline in the active force generation capability of skeletal muscle. While many factors related to the changes of morphological and structural properties in muscle fibers and the extracellular matrix (ECM) have been considered as possible reasons for causing age‐related force reduction, it is still not fully un...

We present an open-source software RKPM2D for solving PDEs under the reproducing kernel particle method (RKPM)-based meshfree computational framework. Compared to conventional mesh-based methods, RKPM provides many attractive features, such as arbitrary order of continuity and discontinuity, relaxed tie between the quality of the discretization and...

Physics-constrained data-driven computing is a hybrid approach that integrates universal physical laws with data-based models of experimental data to enhance scientific computing. A new data-driven simulation approach enriched with a locally convex reconstruction, termed the local convexity data-driven (LCDD) computing, is proposed to enhance accur...

A stable and nodally integrated meshfree formulation for modeling shock waves in fluids is developed. The reproducing kernel approximation is employed to discretize the conservation equations for compressible flow, and a flux vector splitting approach is applied to allow proper numerical treatments for the advection and pressure parts, respectively...

(Available at http://link.springer.com/article/10.1007/s00466-018-1611-8) In this work, we propose a new decomposed subspace reduction (DSR) method for reduced-order modeling of fracture mechanics based on the integrated singular basis function method (ISBFM) with reproducing kernel approximation enriched by crack-tip basis functions. It is shown t...

Although lap joint laser welding is in great demand in practice, very limited numerical studies have been performed to investigate the hot cracking behavior in this welding configuration. This is because the traditional numerical methods are ineffective in simulating lap joint welding processes, which requires consideration of the discontinuous the...

For a truly meshfree technique, Galerkin meshfree methods rely chiefly on nodal integration of the weak form. In the case of Strong Form Collocation meshfree methods, direct collocation at the nodes can be employed. In this paper, performance of these node-based Galerkin and collocation meshfree methods is compared in terms of accuracy, efficiency,...

Laser welding offers numerous advantages and has been widely adopted in the automotive industry. However, its application in light-weight vehicle manufacturing is still limited due to hot cracking that commonly occurs in Al-Mg-Si alloys. An in-depth understanding of the hot cracking mechanism is of great importance to the optimal design of welding...

In the past two decades, meshfree methods have emerged into a new class of computational methods with considerable success. In addition, a significant amount of progress has been made in addressing the major shortcomings that were present in these methods at the early stages of their development. For instance, essential boundary conditions are almo...

Meshfree approximations are ideal for the gradient-type stabilized Petrov–Galerkin methods used for solving Eulerian conservation laws due to their ability to achieve arbitrary smoothness, however, the gradient terms are computationally demanding for meshfree methods. To address this issue, a stabilization technique that avoids high order different...

Convective transport terms in Eulerian conservation laws lead to numerical instability in the solution of Bubnov-Galerkin methods for these non-self-adjoint PDEs. Stabilized Petrov-Galerkin methods overcome this difficulty, however gradient terms are required to construct the test functions, which are typically expensive for meshfree methods. In th...

A meshfree formulation under the reproducing kernel particle method (RKPM) was introduced for modeling the penetration and perforation of brittle geomaterials such as concrete. RKPM provides a robust framework to effectively model the projectile-target interaction and the material failure and fragmentation behaviors that are critical for this class...

doi:10.1002/nme.5183 Accepted manuscript online: 5 December 2015 Manuscript Accepted: 27 November 2015 Manuscript Revised: 11 November 2015 Manuscript Received: 19 August 2015 Convergent and stable domain integration that is also computationally efficient remains a challenge for Galerkin meshfree methods. High order quadrature can achieve stabili...

This document is a pre-print version of a contribution to Modeling and Simulation in Science, Engineering and Technology Book Series devoted to AFSI 2014 - a birthday celebration conference for Tayfun Tezduyar. Submitted Sep. 24, 2015. Accepted Nov. 19, 2015. Meshfree approximations are ideal for the gradient-type stabilized Petrov- Galerkin metho...

Numerical modeling of reservoirs with low permeability or under undrained conditions often suffers from spurious fluid pressure oscillations due to the improper construction of approximation spaces. To address this issue, a fully coupled, stabilized meshfree formulation is developed based on a fluid pressure projection method, in which an additiona...

This paper introduces the meshfree reproducing kernel particle method for 3D image-based modelling of skeletal muscles. This approach allows for construction of simulation model based on pixel data obtained from medical images. The material properties and muscle fibre direction obtained from diffusion tensor imaging (DTI) are input at each pixel po...

This article presents a novel approach for coupling of Isogeometric Analysis (IGA) and Meshfree discretizations. Taking advantage of the strengths of both techniques, we make IGA responsible for the representation of the exact geometry of the problem domain boundary, while the Reproducing Kernel Particle Method (RKPM) discretization is employed in...

Spline-type approximations for solving partial differential equations are the basis of isogeometric analysis. While the common approach of using integration cells defined by single knot spans using standard (e.g., Gaussian) quadrature rules is sufficient for accuracy, more efficient domain integration is still in high demand. The recently introduce...

Error-controlled adaptive meshfree methods are presented for both global error measures, such as the energy norm, and goal-oriented error measures in terms of quantities of interest. The meshfree method chosen in this paper is the reproducing kernel particle method (RKPM), since it is based on a Galerkin scheme and therefore allows extensions of qu...

This paper introduces the meshfree Reproducing Kernel Particle Method (RKPM) in conjunction with a stabilized conforming nodal integration for 3D image-based modeling of skeletal muscles. This approach allows for construction of simulation model based on pixel data obtained from medical images. The model consists of different materials and muscle f...

Galerkin meshfree methods can suffer from instability and suboptimal convergence if the issue of quadrature is not properly addressed. The instability due to quadrature is further magnified in high strain rate events when nodal integration is used. In this paper, several stable and convergent nodal integration methods are presented and applied to t...

Model order reduction (MOR) techniques for enriched reproducing kernel meshfree methods are proposed for analysis of Poisson problems with mild and strong singularities. The employment of an integrated singular basis function method (ISBFM), in conjunction with the selection of harmonic near-tip asymptotic basis functions, leads to a Galerkin formu...

SUMMARYA natural kernel contact (NKC) algorithm under the framework of the semi-Lagrangian reproducing kernel particle method (semi-Lagrangian RKPM) is proposed to model multi-body contact with specific consideration for impact and penetration modeling. The NKC algorithm utilizes the interaction of the semi-Lagrangian kernel functions associated wi...

This paper presents a meshfree smooth contact formulation for application to metal forming problems. The continuum-based contact formulation requires C2 continuity in the approximation of contact surface geometry and displacement variables, which is difficult for the conventional C0 finite elements. In this work, we introduce a reproducing kernel a...

Mechanical properties of proteins play an important role in their biological function. For example, microtubules carry large loads to transport organelles inside the cell, and virus shells undergo changes in shape and mechanical properties during maturation which affect their infectivity. Various theoretical models including continuum elasticity ha...

Molecular dynamics (MD) systems are highly nonlinear and nonlocal, and the conventional model order reduction methods are ineffective for MD systems. The RBF-POD method (Lee and Chen, 2013) employed a radial basis function (RBF) approximated potential energies and inter-atomic forces of MD systems under the framework of the proper orthogonal decomp...

Model order reduction for molecular dynamics (MD) systems exhibits intrinsic complexities because of the highly nonlinear and nonlocal multi-atomic interactions in high dimensions. In the present work, we introduce a proper orthogonal decomposition-based method in conjunction with the radial basis function (RBF) approximation of the nonlinear and n...

Existing and emerging methods in computational mechanics are rarely validated against problems with an unknown outcome. For this reason, Sandia National Laboratories, in partnership with US National Science Foundation and Naval Surface Warfare Center Carderock Division, launched a computational challenge in mid-summer, 2012. Researchers and enginee...

A novel approach is presented to correct the error from numerical integration in Galerkin methods for meeting linear exactness. This approach is based on a Ritz projection of the integration error that allows a modified Galerkin discretization of the original weak form to be established in terms of assumed strains. The solution obtained by this met...

Because most approximation functions employed in meshfree methods are rational functions with overlapping supports, sufficiently accurate domain integration becomes costly, whereas insufficient accuracy in the domain integration leads to suboptimal convergence. In this paper, we show that it is possible to achieve optimal convergence by enforcing v...

A weighted strong form collocation framework with mixed radial basis approximations for the pressure and displacement fields is proposed for incompressible and nearly incompressible linear elasticity. It is shown that with the proper choice of independent source points and collocation points for the radial basis approximations in the pressure and d...

Strong form collocation with radial basis approximation, called the radial basis collocation method (RBCM), is introduced for the numerical solution of elastodynamics. In this work, the proper weights for the boundary collocation equations to achieve the optimal convergence in elastodynamics are first derived. The von Neumann method is then introdu...

The earlier work in the development of direct strong form collocation methods, such as the reproducing kernel collocation method (RKCM), addressed the domain integration issue in the Galerkin type meshfree method, such as the reproducing kernel particle method, but with increased computational complexity because of taking higher order derivatives o...

Mathematical and computational frameworks for multiscale modeling of bone materials are presented. Asymptotic-based homogenization is first introduced to correlate the microscopic solid-fluid phase composition and properties to the macroscopic generalized Darcy's law and balance laws, where the homogenized macroscopic continuity and equilibrium equ...

Biomaterials are typical structures of poroelasticity in nature. Due to heterogeneous composition of microstructures, the asymptotic based homogenization was introduced to correlate the microscopic solid-fluid phase to the macroscopic balance laws. In this work, a microstructure informed computational method for modeling of porous biomaterials is d...

A wheel experiencing sinkage and slippage events poses a high risk to rover missions as evidenced by recent mobility challenges on the Mars Exploration Rover (MER) project. Because several factors contribute to wheel sinkage and slippage conditions such as soil composition, large deformation soil behavior, wheel geometry, nonlinear contact forces,...