VIP20
本课程属于：029-AI for CAE Simulation VVUQ & Geometric Deep Learning 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

LS-DYNA Simulation Data Preparation for AI Model Training

30 Chapters
View Course Samples Online
01
Course overview
scope, concepts, and technical context of LS-DYNA simulation data preparation for AI model training.
technicalLS-DYNA
02
LS-DYNA simulation workflow fundamentals
keyword input decks, solver execution, and output formats relevant to data generation.
LS-DYNAsimulation
03
Identifying AI training data requirements
defining input features, output targets, and dataset specifications for surrogate models and ML pipelines.
Identifyingtraining
04
Parametric model generation in LS-DYNA
using *PARAMETER, *DEFINE, and scripting to create design variations for dataset diversity.
Parametricgeneration
05
Design of experiments for simulation data
sampling strategies (Latin hypercube, Sobol, factorial) and their implementation for LS-DYNA runs.
experimentssimulation
06
Automating LS-DYNA job submission and monitoring
batch scripts, job schedulers, and error handling for large-scale data generation.
AutomatingLS-DYNA
07
Extracting and parsing LS-DYNA output data
d3plot, binout, and ASCII files; using LS-PrePost and Python APIs for efficient data retrieval.
ExtractingLS-DYNA
08
Data cleaning and quality assurance for simulation datasets
detecting failed runs, outliers, and incomplete results.
cleaningassurance
09
Feature engineering from LS-DYNA results
deriving physically meaningful descriptors (e.g., energy absorption, peak force, deformation metrics) for ML models.
LS-DYNAderiving
10
Handling time-series and field data
downsampling, interpolation, and representation of transient simulation outputs for AI training.
Handlingtime-series
11
Data normalization and scaling techniques for simulation datasets
best practices and pitfalls in the context of LS-DYNA outputs.
normalizationtechniques
12
Managing metadata and provenance
tracking simulation parameters, solver versions, and material models for reproducible AI datasets.
Managingmetadata
13
Efficient storage and compression of large simulation datasets
formats, chunking, and parallel I/O considerations.
Efficientcompression
14
Data splitting strategies for AI model training
training, validation, and test sets considering simulation parameter space.
splittingstrategies
15
Addressing class imbalance and data scarcity in simulation datasets
augmentation and synthetic data generation techniques.
Addressingimbalance
16
Case study
preparing crashworthiness simulation data for training a surrogate model for intrusion prediction.
preparingcrashworthiness
17
Case study
generating and curating LS-DYNA data for material model calibration using machine learning.
generatingcurating
18
Case study
dataset creation for real-time visualization and reduced-order modeling of impact simulations.
creationreal-time
19
Integrating LS-DYNA data preparation into MLOps pipelines
versioning, lineage, and automated retraining triggers.
IntegratingLS-DYNA
20
Parallelization and high-performance computing for large-scale LS-DYNA data generation
job arrays, MPI, and cloud resources.
Parallelizationhigh-performance
21
Data security and compliance in simulation data sharing
anonymization, licensing, and export control considerations.
securitycompliance
22
Quality metrics for AI-ready simulation datasets
completeness, consistency, and representativeness.
AI-readysimulation
23
Tools and frameworks for LS-DYNA data preprocessing
Python libraries (numpy, pandas, h5py), LS-DYNA APIs, and custom scripts.
frameworksLS-DYNA
24
Automating data labeling for supervised learning
deriving ground truth from simulation outputs without manual intervention.
Automatinglabeling
25
Handling multi-fidelity simulation data
combining LS-DYNA runs with different mesh densities or solver settings for AI training.
Handlingmulti-fidelity
26
Data augmentation for simulation datasets
physics-informed transformations and noise injection.
augmentationsimulation
27
Workflow orchestration for end-to-end data preparation
using workflow managers (e.g., Snakemake, Airflow) with LS-DYNA.
Workfloworchestration
28
Performance benchmarking of data preparation pipelines
identifying bottlenecks and optimizing throughput.
Performancebenchmarking
29
Documentation and reproducibility standards for AI-ready simulation datasets
templates, checklists, and FAIR principles.
Documentationreproducibility
30
Future trends and challenges
integration of LS-DYNA with AI platforms, real-time data streaming, and adaptive sampling.
challengesintegration