Neural network surrogate and projected gradient descent for fast and reliable finite element model calibration: A case study on an intervertebral disc.

Matan Atad¹, Gabriel Gruber², Marx Ribeiro³, Luis Fernando Nicolini⁴, Robert Graf⁵, Hendrik Möller⁵, Kati Nispel⁶, Ivan Ezhov⁷, Daniel Rueckert⁸, Jan S Kirschke²

¹Institute for Neuroradiology, TUM University Hospital, School of Medicine and Health, Technical University of Munich (TUM), Munich, Germany; Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany. Electronic address: matan.atad@tum.de.

²Institute for Neuroradiology, TUM University Hospital, School of Medicine and Health, Technical University of Munich (TUM), Munich, Germany.

³Department of Trauma and Reconstructive Surgery, University Hospital Halle, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany; Department of Mechanical Engineering, Federal University of Santa Catarina, Florianópolis, Brazil.

⁴Department of Mechanical Engineering, Federal University of Santa Maria, Av. Santa Maria, Brazil.

⁵Institute for Neuroradiology, TUM University Hospital, School of Medicine and Health, Technical University of Munich (TUM), Munich, Germany; Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany.

⁶Institute for Neuroradiology, TUM University Hospital, School of Medicine and Health, Technical University of Munich (TUM), Munich, Germany; Associate Professorship of Sport Equipment and Sport Materials, School of Engineering and Design, Technical University of Munich, Munich, Germany.

⁷Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany.

⁸Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany; Department of Computing, Imperial College London, London, United Kingdom.

PubMed DOI

Abstract

Accurate calibration of finite element (FE) models is essential across various biomechanical applications, including human intervertebral discs (IVDs), to ensure their reliability and use in diagnosing and planning treatments. However, traditional calibration methods are computationally intensive, requiring iterative, derivative-free optimization algorithms that often take days to converge. This s…

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