Amirreza Khodadadian's Outputs (11)

A Bayesian inversion supervised learning framework for the enzyme activity in graphene field-effect transistors (2025)
Journal Article
Khodadadian, E., Mirsian, S., Shashaani, S., Parvizi, M., Khodadadian, A., Knees, P., Hilber, W., & Heitzinger, C. (2025). A Bayesian inversion supervised learning framework for the enzyme activity in graphene field-effect transistors. Machine Learning with Applications, 21, Article 100718. https://doi.org/10.1016/j.mlwa.2025.100718

Graphene Field-Effect Transistors (GFETs) are gaining prominence in enzyme detection due to their exceptional sensitivity, rapid response, and capability for real-time monitoring of enzymatic reactions. Among different catalytic sys... Read More about A Bayesian inversion supervised learning framework for the enzyme activity in graphene field-effect transistors.

Integrating physics-based simulations, machine learning, and Bayesian inference for accurate detection and metrology of elongated nanoscale analytes using high-frequency capacitance spectroscopy (2025)
Journal Article
Khodadadian, E., Goldoni, D., Nicolini, J., Khodadadian, A., Heitzinger, C., & Selmi, L. (2025). Integrating physics-based simulations, machine learning, and Bayesian inference for accurate detection and metrology of elongated nanoscale analytes using high-frequency capacitance spectroscopy. Engineering Applications of Artificial Intelligence, 159(Part C, 2025), 1-17. https://doi.org/10.1016/j.engappai.2025.111679

Elongated analytes are simple general-purpose model systems for nucleic acid strands, bacteriophages, nanoplastic fibers, nanotubes, nanorods, etc., and are characterized by numerous unknowns (e.g., material composition, length, orientation, etc.) th... Read More about Integrating physics-based simulations, machine learning, and Bayesian inference for accurate detection and metrology of elongated nanoscale analytes using high-frequency capacitance spectroscopy.

Graphene-based FETs for advanced biocatalytic profiling: investigating heme peroxidase activity with machine learning insights (2025)
Journal Article
Mirsian, S., Hilber, W., Khodadadian, E., Parvizi, M., Khodadadian, A., Khoshfetrat, S. M., Heitzinger, C., & Jakoby, B. (in press). Graphene-based FETs for advanced biocatalytic profiling: investigating heme peroxidase activity with machine learning insights. Microchimica Acta, 192(3), 1-15. https://doi.org/10.1007/s00604-025-06955-y

Graphene-based field-effect transistors (GFETs) are rapidly gaining recognition as powerful tools for biochemical analysis due to their exceptional sensitivity and specificity. In this study, we utilize a GFET system to explore the peroxidase-based b... Read More about Graphene-based FETs for advanced biocatalytic profiling: investigating heme peroxidase activity with machine learning insights.

A reproducing kernel particle method (RKPM) algorithm for solving the tropical Pacific Ocean model (2024)
Journal Article
Abbaszadeh, M., Parvizi, M., Khodadadian, A., Wick, T., & Dehghan, M. (in press). A reproducing kernel particle method (RKPM) algorithm for solving the tropical Pacific Ocean model. Computers and Mathematics with Applications, 179, 197-211. https://doi.org/10.1016/j.camwa.2024.12.011

Meshless methods have become increasingly popular for solving a wide range of problems in both solid and fluid mechanics. In this study, we focus on a meshless numerical approach to solve the tropical Pacific Ocean model, which captures the horizonta... Read More about A reproducing kernel particle method (RKPM) algorithm for solving the tropical Pacific Ocean model.

A reduced-order least squares-support vector regression and isogeometric collocation method to simulate Cahn-Hilliard-Navier-Stokes equation (2024)
Journal Article
Abbaszadeh, M., Khodadadian, A., Parvizi, M., Dehghan, M., & Xiao, D. (2025). A reduced-order least squares-support vector regression and isogeometric collocation method to simulate Cahn-Hilliard-Navier-Stokes equation. Journal of Computational Physics, 523, Article 113650. https://doi.org/10.1016/j.jcp.2024.113650

The coupled Cahn-Hilliard-Navier-Stokes equations are employed to model two-phase flow separation. To enhance computational efficiency, the pressure term is eliminated from the system of equations, leveraging the stream and vorticit... Read More about A reduced-order least squares-support vector regression and isogeometric collocation method to simulate Cahn-Hilliard-Navier-Stokes equation.

An Efficient FEniCS implementation for coupling lithium-ion battery charge/discharge processes with fatigue phase-field fracture (2024)
Journal Article
Noii, N., Milijasevic, D., Khodadadian, A., & Wick, T. (2024). An Efficient FEniCS implementation for coupling lithium-ion battery charge/discharge processes with fatigue phase-field fracture. Engineering Fracture Mechanics, 306, Article 110251. https://doi.org/10.1016/j.engfracmech.2024.110251

Accurately predicting the fatigue failure of lithium-ion battery electrode particles during charge–discharge cycles is essential for enhancing their structural reliability and lifespan. The fatigue failure of lithium-ion battery electrode particles d... Read More about An Efficient FEniCS implementation for coupling lithium-ion battery charge/discharge processes with fatigue phase-field fracture.

Fatigue failure theory for lithium diffusion induced fracture in lithium-ion battery electrode particles (2024)
Journal Article
Noii, N., Milijasevic, D., Waisman, H., & Khodadadian, A. (2024). Fatigue failure theory for lithium diffusion induced fracture in lithium-ion battery electrode particles. Computer Methods in Applied Mechanics and Engineering, 428, Article 117068. https://doi.org/10.1016/j.cma.2024.117068

To gain better insights into the structural reliability of lithium-ion battery electrodes and the nucleation as well as propagation of cracks during the charge and discharge cycles, it is crucial to enhance our understanding of the degradation mechan... Read More about Fatigue failure theory for lithium diffusion induced fracture in lithium-ion battery electrode particles.

Investigation of combustion model via the local collocation technique based on moving Taylor polynomial (MTP) approximation/domain decomposition method with error analysis (2023)
Journal Article
Abbaszadeh, M., Khodadadian, A., Parvizi, M., & Dehghan, M. (2024). Investigation of combustion model via the local collocation technique based on moving Taylor polynomial (MTP) approximation/domain decomposition method with error analysis. Engineering Analysis with Boundary Elements, 159, 288-301. https://doi.org/10.1016/j.enganabound.2023.11.010

In this paper, we develop a new meshless numerical procedure for simulating the combustion model. To that end, we employ a local meshless collocation method according to the moving Taylor polynomial (MTP) approximation. The space derivative is approx... Read More about Investigation of combustion model via the local collocation technique based on moving Taylor polynomial (MTP) approximation/domain decomposition method with error analysis.

Probabilistic failure mechanisms via Monte Carlo simulations of complex microstructures (2022)
Journal Article
Noii, N., Khodadadian, A., & Aldakheel, F. (2022). Probabilistic failure mechanisms via Monte Carlo simulations of complex microstructures. Computer Methods in Applied Mechanics and Engineering, 399, Article 115358. https://doi.org/10.1016/j.cma.2022.115358

A probabilistic approach to phase-field brittle and ductile fracture with random material and geometric properties is proposed within this work. In the macroscopic failure mechanics, materials properties and spatial quantities (of different phases in... Read More about Probabilistic failure mechanisms via Monte Carlo simulations of complex microstructures.

Bayesian Inversion with Open-Source Codes for Various One-Dimensional Model Problems in Computational Mechanics (2022)
Journal Article
Noii, N., Khodadadian, A., Ulloa, J., Aldakheel, F., Wick, T., François, S., & Wriggers, P. (2022). Bayesian Inversion with Open-Source Codes for Various One-Dimensional Model Problems in Computational Mechanics. Archives of Computational Methods in Engineering, 29(6), 4285-4318. https://doi.org/10.1007/s11831-022-09751-6

The complexity of many problems in computational mechanics calls for reliable programming codes and accurate simulation systems. Typically, simulation responses strongly depend on material and model parameters, where one distinguishes between backwar... Read More about Bayesian Inversion with Open-Source Codes for Various One-Dimensional Model Problems in Computational Mechanics.

Bayesian inversion for anisotropic hydraulic phase-field fracture (2021)
Journal Article
Noii, N., Khodadadian, A., & Wick, T. (2021). Bayesian inversion for anisotropic hydraulic phase-field fracture. Computer Methods in Applied Mechanics and Engineering, 386, Article 114118. https://doi.org/10.1016/j.cma.2021.114118

In this work, we employ a Bayesian inversion framework to fluid-filled phase-field fracture. We develop a robust and efficient numerical algorithm for hydraulic phase-field fracture toward transversely isotropic and orthotropy anisotropic fracture. I... Read More about Bayesian inversion for anisotropic hydraulic phase-field fracture.