Reiter, Nina
Nina Reiter, M. Sc.
Short Bio
Nina Reiter studied Mechanical Engineering at FAU, where she received her Master’s degree in 2020. During her Bachelor studies, she spent a semester abroad at the University of Cádiz, Spain. As a part of her Master studies, Nina participated in a summer school on sustainable tourism and coral reef restoration at the University of Denpasar, Indonesia. Since 2020, she is pursuing her PhD in the Emmy-Noether research group “BRAINIACS – Brain Mechanics Across Scales”. Her research focuses on developing a constitutive model for brain tissue that is based on the tissue’s microstructural composition.
Nina was elected as a member of the GAMM Juniors – the GAMM’s council of young researchers – for the years 2022-2024 and as the speaker of the GAMM Juniors for the year 2023. Since 2023, she is also an associate member of the CRC 1540 “Exploring Brain Mechanics”.
Nina’s areas of interest include brain microstructure, mechanical testing of soft tissues, and viscoelastic modeling. When she’s not in the lab, Nina likes to spend time in front of her aquarium or to go swimming, dancing, or bouldering.
-
Symplectic Elasticity Theory and Formulation for Geometrically Nonlinear Structures
(Third Party Funds Single)
Term: 1. January 2021 - 31. December 2022
Funding source: Deutscher Akademischer Austauschdienst (DAAD)Die Kontinuumsmechanik ist eine wichtige Grundlagenwissenschaft in den Ingenieur- und Naturwissenschaften, die den Zusammenhang zwischen Kräften und Deformationen (und Bewegungen) in Materialien und Strukturen modelliert. Ihre numerische Umsetzung z.B. in der Finiten Element Methode ist aus dem Alltag von Berechnungsabteilungen von technologieorientierten Unternehmen aufgrund ihrer hervorgehobenen Relevanz heutzutage nicht mehr wegzudenken. Das hier beantragte Vorhaben zielt, motiviert durch Konzepte der Hamiltonschen Dynamik auf die erstmalige Etablierung eines völlig neuartigen, sogenannten symplektischen Zugangs zur geometrisch nichtlinearen Kontinuumsmechanik mit zunächst speziellem Fokus auf die nichtlineare Elastizität. Die symplektische Formulierung der geometrisch nichtlinearen Kontinuumsmechanik verspricht neben ihrer Eleganz dabei insbesondere zahlreiche Vorteile im Rahmen ihrer numerischen Umsetzung. Die nichtlineare Elastizität hat vielfältige bedeutende Modellierungsanwendungen im Bereich weicher und weichster Materialien mit größter aktueller Bedeutung beispielweise für die Mechanik biologischer Gewebe, die Soft-Robotik sowie zahlreicher derzeit entwickelter high-tech Metamaterialien. In Summe wird hier sehr vielversprechendes aber auch riskantes thematisches Neuland betreten, wobei die Erfolgsaussichten des Vorhabens aufgrund der komplementären Expertise der Projektpartner als sehr hoch einzuschätzen sind. -
BRAIn mechaNIcs ACross Scales: Linking microstructure, mechanics and pathology
(Third Party Funds Single)
Term: 1. October 2019 - 30. September 2025
Funding source: DFG-Einzelförderung / Emmy-Noether-Programm (EIN-ENP)
URL: https://www.brainiacs.forschung.fau.de/The current research project aims to develop microstructurallymotivated mechanical models for brain tissue that facilitate early diagnosticsof neurodevelopmental or neurodegenerative diseases and enable the developmentof novel treatment strategies. In a first step, we will experimentallycharacterize the behavior of brain tissue across scales by using versatiletesting techniques on the same sample. Through an accompanying microstructuralanalysis of both cellular and extra-cellular components, we will evaluate thecomplex interplay of brain structure, mechanics and function. We will alsoexperimentally investigate dynamic changes in tissue properties duringdevelopment and disease, due to changes in the mechanical environment of cells (mechanosensing),or external loading. Based on the simultaneous analysis of experimental andmicrostructural data, we will develop microstructurally motivated constitutive lawsfor the regionally varying mechanical behavior of brain tissue. In addition, wewill develop evolution laws that predict remodeling processes duringdevelopment, homeostasis, and disease. Through the implementation within afinite element framework, we will simulate the behavior of brain tissue underphysiological and pathological conditions. We will predict how known biologicalprocesses on the cellular scale, such as changes in the tissue’smicrostructure, translate into morphological changes on the macroscopic scale,which are easily detectable through modern imaging techniques. We will analyzeprogression of disease or mechanically-induced loss of brain function. The novelexperimental procedures on the borderline of mechanics and biology, togetherwith comprehensive theoretical and computational models, will form thecornerstone for predictive simulations that improve early diagnostics of pathologicalconditions, advance medical treatment strategies, and reduce the necessity ofanimal and human tissue experimentation. The established methodology will furtheropen new pathways in the biofabrication of artificial organs. -
Multiscale modeling of nervous tissue: comprehensively linking microstructure, pathology, and mechanics
(FAU Funds)
Term: 1. July 2018 - 30. June 2019
2024
Model-driven exploration of poro-viscoelasticity in human brain tissue: be careful with the parameters!
In: Interface Focus 14 (2024)
ISSN: 2042-8901
DOI: 10.1098/rsfs.2024.0026
, , , , , , , , :
Identifying composition-mechanics relations in human brain tissue based on neural-network-enhanced inverse parameter identification
In: Mathematics and Mechanics of Solids (2024)
ISSN: 1081-2865
DOI: 10.1177/10812865231206544
, , , , , , :
Using dropout based active learning and surrogate models in the inverse viscoelastic parameter identification of human brain tissue
In: Frontiers in Physiology 15 (2024), Article No.: 1321298
ISSN: 1664-042X
DOI: 10.3389/fphys.2024.1321298
, , , :
2023
Automated discovery of interpretable hyperelastic material models for human brain tissue with EUCLID
In: Journal of the Mechanics and Physics of Solids 180 (2023), Article No.: 105404
ISSN: 0022-5096
DOI: 10.1016/j.jmps.2023.105404
, , , , , , , :
On the importance of using region-dependent material parameters for full-scale human brain simulations
In: European Journal of Mechanics A-Solids 99 (2023), Article No.: 104910
ISSN: 0997-7538
DOI: 10.1016/j.euromechsol.2023.104910
, , , :
Inverse identification of region-specific hyperelastic material parameters for human brain tissue
In: Biomechanics and Modeling in Mechanobiology (2023)
ISSN: 1617-7959
DOI: 10.1007/s10237-023-01739-w
, , , , , :
Inverse identification of region-specific hyperelastic material parameters for human brain tissue
In: Biomechanics and modeling in mechanobiology (2023)
ISSN: 1617-7940
DOI: 10.1007/s10237-023-01739-w
, , , , , :
Mechanisms of mechanical load transfer through brain tissue
In: Scientific Reports 13 (2023), Article No.: 8703
ISSN: 2045-2322
DOI: 10.1038/s41598-023-35768-3
, , :
Modeling the finite viscoelasticity of human brain tissue based on microstructural information
In: Proceedings in Applied Mathematics and Mechanics (2023)
ISSN: 1617-7061
DOI: 10.1002/pamm.202300234
, , , , :
2022
Tissue-Scale Biomechanical Testing of Brain Tissue for the Calibration of Nonlinear Material Models
In: Current Protocols 2 (2022), p. e381-
ISSN: 2691-1299
DOI: 10.1002/cpz1.381
, , , , :
Oxidized Hyaluronic Acid-Gelatin-Based Hydrogels for Tissue Engineering and Soft Tissue Mimicking
In: Tissue Engineering - Part C: Methods (2022)
ISSN: 1937-3384
DOI: 10.1089/ten.tec.2022.0004
, , , , , , , :
Statistical interpretation of LDA measurements in naturally developing turbulent drag-reducing flow using invariant theory
In: International Journal of Heat and Fluid Flow 93 (2022), Article No.: 108856
ISSN: 0142-727X
DOI: 10.1016/j.ijheatfluidflow.2021.108856
, , , , , , :
2021
Poro-Viscoelastic Effects During Biomechanical Testing of Human Brain Tissue
In: Frontiers in Mechanical Engineering 7 (2021), Article No.: 708350
ISSN: 2297-3079
DOI: 10.3389/fmech.2021.708350
, , , , , , :
Unraveling the Local Relation Between Tissue Composition and Human Brain Mechanics Through Machine Learning
In: Frontiers in Bioengineering and Biotechnology 9 (2021)
ISSN: 2296-4185
DOI: 10.3389/fbioe.2021.704738
, , , , , , , :
Insights into the Microstructural Origin of Brain Viscoelasticity
In: Journal of Elasticity 145 (2021), p. 99-116
ISSN: 0374-3535
DOI: 10.1007/s10659-021-09814-y
URL: https://link.springer.com/article/10.1007/s10659-021-09814-y
, , , :
Laboratory Training Biomechanics
since 2023