Research graph
References from A Practical Tutorial on Physics‐Informed Networks for Pharmacometrics and Quantitative Systems Pharmacology. Local targets link to admitted publications; unresolved targets remain external evidence.
Modeling of Pharmacokinetic/Pharmacodynamic (PK/PD) Relationships: Concepts and Perspectives
10.1023/a:1011907920641 · 1999 · External reference
Structural Identifiability of Physiologically Based Pharmacokinetic Models
10.1007/s10928-006-9011-7 · 2006 · External reference
Physics‐Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations
10.1016/j.jcp.2018.10.045 · 2019 · External reference
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Learning Chemotherapy Drug Action via Universal Physics‐Informed Neural Networks
10.1007/s11095-025-03858-8 · 2025 · External reference
Using Physics‐Informed Neural Networks (PINNs) for Tumor Cell Growth Modeling
10.3390/math12081195 · 2024 · External reference
Physics‐Informed Neural Network for Parameter Inference in a Tumor Model
10.3390/math14071102 · 2026 · External reference
Representation Meets Optimization: Training PINNs and PIKANs for Gray‐Box Discovery in Systems Pharmacology
10.1016/j.compbiomed.2025.111393 · 2026 · External reference
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AI‐Aristotle: A Physics‐Informed Framework for Systems Biology Gray‐Box Identification
10.1371/journal.pcbi.1011916 · 2024 · External reference
CMINNs: Compartment Model Informed Neural Networks—Unlocking Drug Dynamics
10.1016/j.compbiomed.2024.109392 · 2025 · External reference
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Drug Release Modeling Using Physics‐Informed Neural Networks
10.1016/j.jddst.2026.108654 · 2026 · External reference
Physics‐Informed Machine Learning in Biomedical Science and Engineering
10.1146/annurev-bioeng-110824-124907 · 2026 · External reference
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2024 · External reference
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Structural and Practical Identifiability Analysis of Partially Observed Dynamical Models by Exploiting the Profile Likelihood
10.1093/bioinformatics/btp358 · 2009 · External reference
On Structural and Practical Identifiability
10.1016/j.coisb.2021.03.005 · 2021 · External reference
Universally Sloppy Parameter Sensitivities in Systems Biology Models
10.1371/journal.pcbi.0030189 · 2007 · External reference
Determination of Parameter Identifiability in Nonlinear Biophysical Models: A Bayesian Approach
10.1085/jgp.201311116 · 2014 · External reference
10.1016/j.mbs.2026.109745
10.1016/j.mbs.2026.109745 · External reference
10.64898/2026.04.20.719505
10.64898/2026.04.20.719505 · External reference
A Comprehensive and FAIR Comparison Between MLP and KAN Representations for Differential Equations and Operator Networks
10.1016/j.cma.2024.117290 · 2024 · External reference
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Spectral Bias in Physics‐Informed and Operator Learning: Analysis and Mitigation Guidelines
10.1016/j.cma.2026.119156 · 2026 · External reference
From PINNs to PIKANs: Recent Advances in Physics‐Informed Machine Learning
10.1007/s44379-025-00015-1 · 2025 · External reference
Residual‐Based Attention in Physics‐Informed Neural Networks
10.1016/j.cma.2024.116805 · 2024 · External reference
A Comprehensive Study of Non‐Adaptive and Residual‐Based Adaptive Sampling for Physics‐Informed Neural Networks
10.1016/j.cma.2022.115671 · 2023 · External reference
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2023 · External reference
Efficient Training of Physics‐Informed Neural Networks via Importance Sampling
10.1111/mice.12685 · 2021 · External reference
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Failure‐Informed Adaptive Sampling for PINNs
10.1137/22m1527763 · 2023 · External reference
Failure‐Informed Adaptive Sampling for PINNs, Part II: Combining With Re‐Sampling and Subset Simulation
2023 · External reference
Self‐Adaptive Weighting and Sampling for Physics‐Informed Neural Networks
2026 · External reference
Optimizing the Optimizer for Physics‐Informed Neural Networks and Kolmogorov‐Arnold Networks
10.1016/j.cma.2025.118308 · 2025 · External reference
10.1016/j.cma.2026.119289
10.1016/j.cma.2026.119289 · External reference
Systems Biology Informed Deep Learning for Inferring Parameters and Hidden Dynamics
10.1371/journal.pcbi.1007575 · 2020 · External reference
Hidden Fluid Mechanics: Learning Velocity and Pressure Fields From Flow Visualizations
10.1126/science.aaw4741 · 2020 · External reference
Understanding and Mitigating Gradient Flow Pathologies in Physics‐Informed Neural Networks
10.1137/20m1318043 · 2021 · External reference
Self‐Adaptive Loss Balanced Physics‐Informed Neural Networks
10.1016/j.neucom.2022.05.015 · 2022 · External reference
A Dual‐Dimer Method for Training Physics‐Constrained Neural Networks With Minimax Architecture
10.1016/j.neunet.2020.12.028 · 2021 · External reference
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When and Why PINNs Fail to Train: A Neural Tangent Kernel Perspective
10.1016/j.jcp.2021.110768 · 2022 · External reference
Self‐Adaptive Physics‐Informed Neural Networks
10.1016/j.jcp.2022.111722 · 2023 · External reference
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2023 · External reference
Physics and Equality Constrained Artificial Neural Networks: Application to Forward and Inverse Problems With Multi‐Fidelity Data Fusion
10.1016/j.jcp.2022.111301 · 2022 · External reference
10.2139/ssrn.4484779
10.2139/ssrn.4484779 · External reference
Enhanced Physics‐Informed Neural Networks With Augmented Lagrangian Relaxation Method (AL‐PINNs)
10.1016/j.neucom.2023.126424 · 2023 · External reference
Correcting Model Misspecification in Physics‐Informed Neural Networks (PINNs)
10.1016/j.jcp.2024.112918 · 2024 · External reference
Using Machine Learning Surrogate Modeling for Faster QSP VP Cohort Generation
2023 · External reference
State‐Space Models Are Accurate and Efficient Neural Operators for Dynamical Systems
2025 · External reference
Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisons
10.1016/j.jcp.2022.111902 · 2023 · External reference
