Shape-informed cardiac mechanics surrogates in data-scarce regimes via geometric encoding and generative augmentation

• URL: http://arxiv.org/abs/2602.20306v1
• Date: Mon, 23 Feb 2026 19:40:55 GMT
• Title: Shape-informed cardiac mechanics surrogates in data-scarce regimes via geometric encoding and generative augmentation
• Authors: Davide Carrara, Marc Hirschvogel, Francesca Bonizzoni, Stefano Pagani, Simone Pezzuto, Francesco Regazzoni,
• Abstract summary: Surrogate models can accelerate simulations, but generalization across diverse anatomies is challenging.<n>We propose a two-step framework that decouples geometric representation from learning the physics response.<n>We show that the proposed approaches allow for accurate predictions and generalization to unseen geometries.
• Score: 3.03918153717041
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High-fidelity computational models of cardiac mechanics provide mechanistic insight into the heart function but are computationally prohibitive for routine clinical use. Surrogate models can accelerate simulations, but generalization across diverse anatomies is challenging, particularly in data-scarce settings. We propose a two-step framework that decouples geometric representation from learning the physics response, to enable shape-informed surrogate modeling under data-scarce conditions. First, a shape model learns a compact latent representation of left ventricular geometries. The learned latent space effectively encodes anatomies and enables synthetic geometries generation for data augmentation. Second, a neural field-based surrogate model, conditioned on this geometric encoding, is trained to predict ventricular displacement under external loading. The proposed architecture performs positional encoding by using universal ventricular coordinates, which improves generalization across diverse anatomies. Geometric variability is encoded using two alternative strategies, which are systematically compared: a PCA-based approach suitable for working with point cloud representations of geometries, and a DeepSDF-based implicit neural representation learned directly from point clouds. Overall, our results, obtained on idealized and patient-specific datasets, show that the proposed approaches allow for accurate predictions and generalization to unseen geometries, and robustness to noisy or sparsely sampled inputs.

Related papers

• Learning geometry-dependent lead-field operators for forward ECG modeling [3.2203648406499816]
  We propose a shape-informed surrogate model of the lead-field operator that serves as a drop-in replacement for the full-order model in ECG simulations.<n>The proposed method achieves high accuracy in approximating lead fields both within the torso and inside the heart, resulting in highly accurate ECG simulations.
  arXiv  Detail & Related papers  (2026-02-25T20:01:08Z)
• Advanced Geometric Correction Algorithms for 3D Medical Reconstruction: Comparison of Computed Tomography and Macroscopic Imaging [0.9395222766576343]
  This paper introduces a hybrid two-stage registration framework for reconstructing 3D kidney anatomy from macroscopic slices.<n>It addresses the data-scarcity and high-distortion challenges typical of macroscopic imaging.<n>The proposed framework generalizes to other soft-tissue organs reconstructed from optical or photographic cross-sections.
  arXiv  Detail & Related papers  (2026-01-30T17:16:17Z)
• Deformable registration and generative modelling of aortic anatomies by auto-decoders and neural ODEs [4.299467172990462]
  This work introduces AD-SVFD, a deep learning model for the registration of shapes to a pre-defined reference and for the generation of synthetic anatomies.<n>A distinctive feature of AD-SVFD is its auto-decoder structure, that enables generalization across shape cohorts and favors efficient weight sharing.<n>The use of implicit shape representations enables generative applications: new anatomies can be synthesized by suitably sampling from the latent space and applying the corresponding inverse transformations to the reference geometry.
  arXiv  Detail & Related papers  (2025-06-01T10:30:58Z)
• Full-field surrogate modeling of cardiac function encoding geometric variability [1.3643562541556224]
  We propose a novel computational pipeline to embed cardiac anatomies into full-field surrogate models.<n>We use Branched Latent Neural Maps (BLNMs) as an effective scientific machine learning method to encode activation maps extracted from physics-based numerical simulations into a neural network.<n>Our surrogate model demonstrates robustness and great generalization across the complex original patient cohort, achieving an average a mean squared error of 0.0034.
  arXiv  Detail & Related papers  (2025-04-29T07:22:06Z)
• Geometry-aware Active Learning of Spatiotemporal Dynamic Systems [4.251030047034566]
  This paper proposes a geometry-aware active learning framework for modeling dynamic systems.<n>We develop an adaptive active learning strategy to strategically identify spatial locations for data collection and further maximize the prediction accuracy.
  arXiv  Detail & Related papers  (2025-04-26T19:56:38Z)
• A Geometry-Aware Message Passing Neural Network for Modeling Aerodynamics over Airfoils [61.60175086194333]
  aerodynamics is a key problem in aerospace engineering, often involving flows interacting with solid objects such as airfoils.<n>Here, we consider modeling of incompressible flows over solid objects, wherein geometric structures are a key factor in determining aerodynamics.<n>To effectively incorporate geometries, we propose a message passing scheme that efficiently and expressively integrates the airfoil shape with the mesh representation.<n>These design choices lead to a purely data-driven machine learning framework known as GeoMPNN, which won the Best Student Submission award at the NeurIPS 2024 ML4CFD Competition, placing 4th overall.
  arXiv  Detail & Related papers  (2024-12-12T16:05:39Z)
• Discovering Interpretable Physical Models using Symbolic Regression and Discrete Exterior Calculus [55.2480439325792]
  We propose a framework that combines Symbolic Regression (SR) and Discrete Exterior Calculus (DEC) for the automated discovery of physical models. DEC provides building blocks for the discrete analogue of field theories, which are beyond the state-of-the-art applications of SR to physical problems. We prove the effectiveness of our methodology by re-discovering three models of Continuum Physics from synthetic experimental data.
  arXiv  Detail & Related papers  (2023-10-10T13:23:05Z)
• Automatic Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning [60.69217130006758]
  We propose two deep learning models that fully automate shape parameterization for aerodynamic shape optimization. Both models are optimized to parameterize via deep geometric learning to embed human prior knowledge into learned geometric patterns. We perform shape optimization experiments on 2D airfoils and discuss the applicable scenarios for the two models.
  arXiv  Detail & Related papers  (2023-05-03T13:45:40Z)
• Data-driven reduced-order modelling for blood flow simulations with geometry-informed snapshots [0.0]
  A data-driven surrogate model is proposed for the efficient prediction of blood flow simulations on similar but distinct domains. A non-intrusive reduced-order model for geometrical parameters is constructed using proper decomposition. A radial basis function interpolator is trained for predicting the reduced coefficients of the reduced-order model.
  arXiv  Detail & Related papers  (2023-02-21T21:18:17Z)
• A Generative Shape Compositional Framework to Synthesise Populations of Virtual Chimaeras [52.33206865588584]
  We introduce a generative shape model for complex anatomical structures, learnable from datasets of unpaired datasets. We build virtual chimaeras from databases of whole-heart shape assemblies that each contribute samples for heart substructures. Our approach significantly outperforms a PCA-based shape model (trained with complete data) in terms of generalisability and specificity.
  arXiv  Detail & Related papers  (2022-10-04T13:36:52Z)
• Deep learning-based surrogate model for 3-D patient-specific computational fluid dynamics [6.905238157628892]
  It is notoriously challenging to parameterize the input space of arbitrarily complex 3-D geometries. We propose a novel deep learning surrogate modeling solution to address these challenges and enable rapid hemodynamic predictions.
  arXiv  Detail & Related papers  (2022-04-11T17:34:51Z)
• NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go [109.88509362837475]
  We present NeuroMorph, a new neural network architecture that takes as input two 3D shapes. NeuroMorph produces smooth and point-to-point correspondences between them. It works well for a large variety of input shapes, including non-isometric pairs from different object categories.
  arXiv  Detail & Related papers  (2021-06-17T12:25:44Z)
• MetaSDF: Meta-learning Signed Distance Functions [85.81290552559817]
  Generalizing across shapes with neural implicit representations amounts to learning priors over the respective function space. We formalize learning of a shape space as a meta-learning problem and leverage gradient-based meta-learning algorithms to solve this task.
  arXiv  Detail & Related papers  (2020-06-17T05:14:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.