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Geometric Deep Learning for Crash Deformation Fields

30 Chapters

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Crash deformation fields

physical meaning, measurement modalities, and representation as displacement, strain, or point-cloud fields.

deformationphysical

Geometric deep learning foundations

graphs, meshes, point clouds, and manifolds as domains for deformation learning.

Geometricfoundations

Data acquisition and preprocessing for crash deformation

DIC, photogrammetry, laser scanning, and mesh registration pipelines.

acquisitionpreprocessing

Graph neural networks for mesh-based deformation fields

message passing, edge features, and node-level displacement prediction.

networksmesh-based

Point-cloud networks for crash deformation

PointNet++, KPConv, and continuous convolution on irregular post-impact geometries.

Point-cloudnetworks

Mesh convolutional operators

geodesic vs. Euclidean kernels, spectral methods, and anisotropic diffusion for thin-shell deformation.

convolutionaloperators

Equivariant and invariant architectures

SE(3)-equivariant GNNs and group convolutions for physically consistent deformation fields.

Equivariantinvariant

Implicit neural representations for deformation fields

SIREN, NeRF-style coordinate MLPs, and mesh-free field reconstruction.

Implicitrepresentations

Physics-informed geometric learning

incorporating equilibrium, compatibility, and constitutive constraints into GNN losses.

Physics-informedgeometric

Graph-based surrogate modeling for crash simulation

replacing explicit FE solvers with learned message-passing dynamics.

Graph-basedsurrogate

Temporal geometric models for crash evolution

spatio-temporal GNNs and sequence models for deformation time series.

Temporalgeometric

Handling topological changes in crash deformation

fracture, contact, and separation with dynamic graph rewiring.

Handlingtopological

Multi-scale geometric learning

hierarchical GNNs and pooling for coarse-to-fine deformation field prediction.

Multi-scalegeometric

Transfer learning across crash scenarios

domain adaptation for vehicle models, impact locations, and material variations.

Transferscenarios

Uncertainty quantification in geometric deformation prediction

Bayesian GNNs, ensembles, and conformal prediction.

Uncertaintyquantification

Inverse problems with geometric deep learning

inferring impact conditions or material parameters from deformation fields.

problemsgeometric

Real-time crash deformation prediction

model compression, graph coarsening, and latency-aware architecture design.

Real-timedeformation

Benchmarking and metrics for crash deformation fields

Chamfer distance, Hausdorff, strain error, and physical validity checks.

Benchmarkingdeformation

frontal impact deformation prediction on a full vehicle mesh using graph neural networks.

deformationprediction

side-pole impact and door intrusion estimation with point-cloud-based geometric learning.

side-poleintrusion

pedestrian head impact and hood deformation field regression using mesh CNNs.

pedestriandeformation

battery pack crush deformation in EV underbody impacts with equivariant GNNs.

deformationunderbody

aerospace composite panel impact damage prediction using physics-informed point-cloud networks.

aerospacecomposite

train crashworthiness and occupant safety assessment via temporal geometric surrogates.

crashworthinessoccupant

Integration with CAE workflows

coupling learned deformation fields with explicit FE solvers and CAD systems.

Data generation and augmentation strategies

synthetic FE datasets, mesh morphing, and physics-based augmentation.

generationaugmentation

Scalability and generalization

large-scale graph training, out-of-distribution crash scenarios, and robustness.

Scalabilitygeneralization

Interpretability and validation of geometric deep learning models for certification-relevant crash analysis.

Interpretability and validation of geometric deep learning models for certification-relevant crash analysis.

Interpretabilityvalidation

Deployment considerations

ONNX export, GPU inference, and embedded systems for crash sensor and digital twin applications.

Deploymentconsiderations

Capstone project

end-to-end geometric deep learning pipeline for a real crash deformation dataset with evaluation and reporting.

Capstoneend-to-end