Wenrui Hao
YOU?
Author Swipe
View article: Data-driven modeling of amyloid-β targeted antibodies for Alzheimer’s disease
Data-driven modeling of amyloid-β targeted antibodies for Alzheimer’s disease Open
Alzheimer's disease (AD) is characterized by the accumulation of amyloid beta, which is strongly associated with disease progression and cognitive decline. Despite the approval of monoclonal antibodies targeting Aβ, optimizing treatment st…
View article: Correction to: Automatic Differentiation Is Essential in Training Neural Networks for Solving Differential Equations
Correction to: Automatic Differentiation Is Essential in Training Neural Networks for Solving Differential Equations Open
View article: Data-driven spatiotemporal modeling reveals personalized trajectories of cortical atrophy in Alzheimer's disease.
Data-driven spatiotemporal modeling reveals personalized trajectories of cortical atrophy in Alzheimer's disease. Open
Alzheimer's disease (AD) is characterized by the progressive spread of pathology across brain networks, yet forecasting this cascade at the individual level remains challenging. We present a personalized graph-based dynamical model that ca…
View article: Identifiability-Guided Assessment of Digital Twins in Alzheimer’s Disease Clinical Research and Care
Identifiability-Guided Assessment of Digital Twins in Alzheimer’s Disease Clinical Research and Care Open
Digital twins – personalized, data-driven computational models – are emerging as a powerful paradigm for representing and predicting disease trajectories at the individual level. These models have the potential to support diagnosis, monito…
View article: Learning Patient-Specific Spatial Biomarker Dynamics via Operator Learning for Alzheimer's Disease Progression.
Learning Patient-Specific Spatial Biomarker Dynamics via Operator Learning for Alzheimer's Disease Progression. Open
Alzheimer's disease (AD) is a complex, multifactorial neurodegenerative disorder with substantial heterogeneity in progression and treatment response. Despite recent therapeutic advances, predictive models capable of accurately forecasting…
View article: A Systematic Computational Framework for Practical Identifiability Analysis in Mathematical Models Arising from Biology
A Systematic Computational Framework for Practical Identifiability Analysis in Mathematical Models Arising from Biology Open
Practical identifiability is a fundamental challenge in the data‐driven modeling of biological systems, as many model parameters cannot be directly measured and must be estimated from experimental data. Without confirming the identifiabili…
View article: Oral Indomethacin for Chronic Pancreatitis: Results From the PAIR Randomized Placebo-Controlled Trial
Oral Indomethacin for Chronic Pancreatitis: Results From the PAIR Randomized Placebo-Controlled Trial Open
INTRODUCTION: Chronic pancreatitis (CP) remains difficult to manage with few treatment options. Prior studies have implicated prostaglandin E 2 (PGE 2 ) in mediating chronic inflammation in the pancreas. Therefore, we aimed to evaluate whe…
View article: Automatic Differentiation is Essential in Training Neural Networks for Solving Differential Equations
Automatic Differentiation is Essential in Training Neural Networks for Solving Differential Equations Open
View article: ZENN: A Thermodynamics-Inspired Computational Framework for Heterogeneous Data-Driven Modeling
ZENN: A Thermodynamics-Inspired Computational Framework for Heterogeneous Data-Driven Modeling Open
Traditional entropy-based methods - such as cross-entropy loss in classification problems - have long been essential tools for representing the information uncertainty and physical disorder in data and for developing artificial intelligenc…
View article: Data-Driven Modeling of Amyloid-beta Targeted Antibodies for Alzheimer’s Disease
Data-Driven Modeling of Amyloid-beta Targeted Antibodies for Alzheimer’s Disease Open
View article: Optimal error estimates of the diffuse domain method for parabolic equations
Optimal error estimates of the diffuse domain method for parabolic equations Open
In this paper, we study the convergence behavior of the diffuse domain method (DDM) for solving a class of second-order parabolic partial differential equations with Neumann boundary condition posed on general irregular domains. The DDM em…
View article: Accelerating Causal Network Discovery of Alzheimer Disease Biomarkers via Scientific Literature-based Retrieval Augmented Generation
Accelerating Causal Network Discovery of Alzheimer Disease Biomarkers via Scientific Literature-based Retrieval Augmented Generation Open
The causal relationships between biomarkers are essential for disease diagnosis and medical treatment planning. One notable application is Alzheimer's disease (AD) diagnosis, where certain biomarkers may influence the presence of others, e…
View article: Insect medicines for colorectal cancer: A review of mechanisms, preclinical evidence, and future prospects
Insect medicines for colorectal cancer: A review of mechanisms, preclinical evidence, and future prospects Open
Colorectal cancer is recognized as the third most prevalent malignant tumor globally. The recommended treatment modalities, including surgery, radiotherapy, and chemotherapy, are frequently associated with severe side effects and high recu…
View article: Data-Driven Modeling of Amyloid-beta Targeted Antibodies for Alzheimer's Disease
Data-Driven Modeling of Amyloid-beta Targeted Antibodies for Alzheimer's Disease Open
Alzheimer's disease (AD) is driven by the accumulation of amyloid-beta (Abeta) proteins in the brain, leading to memory loss and cognitive decline. While monoclonal antibodies targeting Abetahave been approved, optimizing their use to maxi…
View article: Laplacian Eigenfunction-Based Neural Operator for Learning Nonlinear Reaction-Diffusion Dynamics
Laplacian Eigenfunction-Based Neural Operator for Learning Nonlinear Reaction-Diffusion Dynamics Open
Learning reaction-diffusion equations has become increasingly important across scientific and engineering disciplines, including fluid dynamics, materials science, and biological systems. In this work, we propose the Laplacian Eigenfunctio…
View article: Learn Singularly Perturbed Solutions via Homotopy Dynamics
Learn Singularly Perturbed Solutions via Homotopy Dynamics Open
Solving partial differential equations (PDEs) using neural networks has become a central focus in scientific machine learning. Training neural networks for singularly perturbed problems is particularly challenging due to certain parameters…
View article: An Imbalanced Learning-based Sampling Method for Physics-informed Neural Networks
An Imbalanced Learning-based Sampling Method for Physics-informed Neural Networks Open
This paper introduces Residual-based Smote (RSmote), an innovative local adaptive sampling technique tailored to improve the performance of Physics-Informed Neural Networks (PINNs) through imbalanced learning strategies. Traditional residu…
View article: Homotopy Relaxation Training Algorithms for Infinite-Width Two-Layer ReLU Neural Networks
Homotopy Relaxation Training Algorithms for Infinite-Width Two-Layer ReLU Neural Networks Open
View article: Laplacian Eigenfunction-Based Neural Operator for Learning Nonlinear Reaction–Diffusion Dynamics
Laplacian Eigenfunction-Based Neural Operator for Learning Nonlinear Reaction–Diffusion Dynamics Open
View article: Multiscale Neural Networks for Approximating Green's Functions
Multiscale Neural Networks for Approximating Green's Functions Open
Neural networks (NNs) have been widely used to solve partial differential equations (PDEs) in the applications of physics, biology, and engineering. One effective approach for solving PDEs with a fixed differential operator is learning Gre…
View article: Multifaceted role of haptoglobin: Implications for disease development
Multifaceted role of haptoglobin: Implications for disease development Open
Haptoglobin, a protein primarily recognized for its role in sequestering free hemoglobin, has been identified as a molecule with diverse and underexplored functions in the pathophysiology of various diseases. This editorial explores the mu…
View article: On pattern formation in the thermodynamically-consistent variational Gray-Scott model
On pattern formation in the thermodynamically-consistent variational Gray-Scott model Open
In this paper, we explore pattern formation in a four-species variational Gary-Scott model, which includes all reverse reactions and introduces a virtual species to describe the birth-death process in the classical Gray-Scott model. This m…
View article: Stability and Robustness of Time-discretization Schemes for the Allen-Cahn Equation via Bifurcation and Perturbation Analysis
Stability and Robustness of Time-discretization Schemes for the Allen-Cahn Equation via Bifurcation and Perturbation Analysis Open
The Allen-Cahn equation is a fundamental model for phase transitions, offering critical insights into the dynamics of interface evolution in various physical systems. This paper investigates the stability and robustness of frequently utili…
View article: Companion-based multi-level finite element method for computing multiple solutions of nonlinear differential equations
Companion-based multi-level finite element method for computing multiple solutions of nonlinear differential equations Open
View article: Gauss Newton Method for Solving Variational Problems of PDEs with Neural Network Discretizaitons
Gauss Newton Method for Solving Variational Problems of PDEs with Neural Network Discretizaitons Open
View article: Automatic Differentiation is Essential in Training Neural Networks for Solving Differential Equations
Automatic Differentiation is Essential in Training Neural Networks for Solving Differential Equations Open
Neural network-based approaches have recently shown significant promise in solving partial differential equations (PDEs) in science and engineering, especially in scenarios featuring complex domains or incorporation of empirical data. One …
View article: Newton Informed Neural Operator for Computing Multiple Solutions of Nonlinear Partials Differential Equations
Newton Informed Neural Operator for Computing Multiple Solutions of Nonlinear Partials Differential Equations Open
Solving nonlinear partial differential equations (PDEs) with multiple solutions using neural networks has found widespread applications in various fields such as physics, biology, and engineering. However, classical neural network methods …
View article: HomPINNs: Homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions
HomPINNs: Homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions Open
View article: Personalized Computational Causal Modeling of the Alzheimer Disease Biomarker Cascade
Personalized Computational Causal Modeling of the Alzheimer Disease Biomarker Cascade Open
Results support the feasibility of personalizing mechanistic models based on individual biomarker trajectories and suggest that this approach may be useful for reclassifying subjects on the Alzheimer's clinical spectrum. This computational…
View article: An Imbalanced Learning-Based Sampling Method Forphysics-Informed Neural Networks
An Imbalanced Learning-Based Sampling Method Forphysics-Informed Neural Networks Open