VIP20
本课程属于：009-AI and Machine Learning for Automotive CAE English Technical Courses · 共 20 门相关课程，组合学习更完整。
查看所属VIP全部课程 →
← 返回所属目录 咨询 / 购买

Physics-Informed Neural Networks for Automotive Structures

30 Chapters
View Course Samples Online
01
Automotive structures as physics problems
body-in-white, chassis, battery enclosures, and crash-relevant components; why PINNs are considered alongside FEA and CFD.
Automotivestructures
02
Governing equations for automotive solids and structures
linear elasticity, elastodynamics, modal analysis, and geometric nonlinearity in sheet-metal assemblies.
Governingequations
03
PINN architecture for structural mechanics
coordinate inputs, displacement and stress outputs, multilayer perceptrons, and automatic differentiation for strain and equilibrium residuals.
PINNarchitecture
04
Loss function design for automotive structures
PDE residuals, boundary conditions, initial conditions, and data-driven terms for strain gauges and accelerometers.
functionautomotive
05
Training strategies for PINNs in structural analysis
collocation point sampling, adaptive weighting, curriculum learning, and convergence diagnostics.
Trainingstrategies
06
Linear static analysis of automotive brackets and mounts
PINN formulation, boundary condition enforcement, and comparison with Nastran or Abaqus results.
analysisautomotive
07
Modal analysis of body panels and subframes
eigenvalue problems, frequency-domain residuals, and PINN prediction of natural frequencies and mode shapes.
analysissubframes
08
Dynamic response of automotive structures
time-domain PINNs for transient loads, road excitation, and impact-induced vibration.
responseautomotive
09
Nonlinear structural behavior in automotive components
plastic hinges, buckling of thin-walled sections, and PINN treatment of material and geometric nonlinearity.
Nonlinearstructural
10
Contact and boundary nonlinearity in automotive assemblies
spot welds, bolted joints, and PINN strategies for non-penetration and friction constraints.
boundarynonlinearity
11
Crashworthiness and occupant safety
PINN surrogates for frontal, side, and rear impact simulations; energy absorption and intrusion prediction.
Crashworthinessoccupant
12
Battery enclosure and electric vehicle structures
mechanical integrity, vibration, and crash safety of battery packs using PINNs.
enclosureelectric
13
Fatigue and durability analysis of automotive structures
cyclic loading, S-N curves, and PINN-based life prediction for welded and bolted joints.
durabilityanalysis
14
Thermal-mechanical coupling in automotive structures
exhaust systems, brake components, and battery modules; PINNs for coupled field problems.
Thermal-mechanicalcoupling
15
Fluid-structure interaction in automotive applications
aeroelastic effects, wind noise, and underhood thermal flows with PINN coupling.
Fluid-structureinteraction
16
Multiscale modeling of automotive materials
from microstructure to component-level response using PINNs for homogenization and localization.
Multiscalemodeling
17
Composite and lightweight structures
carbon fiber reinforced polymers, aluminum alloys, and PINN modeling of anisotropic and damage behavior.
Compositelightweight
18
Inverse problems in automotive structures
material parameter identification, load reconstruction, and damage detection from sparse sensor data.
problemsautomotive
19
Uncertainty quantification for PINNs in automotive design
aleatoric and epistemic uncertainty, robust predictions, and reliability assessment.
Uncertaintyquantification
20
Data assimilation and sensor fusion
combining physical models with real-world test data from proving grounds and crash tests.
assimilationcombining
21
PINNs for structural health monitoring
damage detection, localization, and remaining useful life estimation in automotive components.
PINNsstructural
22
Optimization and design exploration
PINN-based surrogates for topology optimization, shape optimization, and lightweight design.
Optimizationexploration
23
Real-time and embedded applications
reduced-order PINNs, model compression, and deployment on vehicle ECUs for active safety systems.
Real-timeembedded
24
Verification and validation of PINNs for automotive structures
benchmarks, error metrics, and comparison with established CAE workflows.
Verificationvalidation
25
Integration with CAD and CAE pipelines
geometry parameterization, mesh generation, and automated PINN training for parametric studies.
IntegrationCAD
26
High-performance computing for PINNs
GPU acceleration, distributed training, and scalability for large-scale automotive models.
High-performancecomputing
27
Case study
PINN for a full vehicle frontal crash simulation; model setup, training, and validation against physical test data.
PINNsimulation
28
Case study
PINN for battery pack vibration and shock analysis; multi-physics coupling and safety assessment.
PINNvibration
29
Case study
PINN for fatigue life prediction of a suspension control arm; data-driven and physics-based hybrid modeling.
PINNprediction
30
Future directions and emerging trends
physics-informed machine learning for autonomous vehicle structures, digital twins, and real-time simulation.
directionsemerging