Skip to main content
远算科技 Yuansuan Technology

YUANSUAN | AI-NATIVE ENGINEERING PLATFORM

From computable to intelligent: an Engineering AI platform for complex engineering.

Engineering intelligence, integrated modeling, and design-space exploration form the core, with trust and validation built into every step.

Explore Platform ArchitectureExplore Products
141Granted invention patents
CMMI Level 5Development process maturity
ISO 27001 · MLPS Level 3Security and compliance

Engineering Loop · Three Systems Working as One

SYS 01 / MODELINGIntegrated ModelingObjects · Physics · Data · ConstraintsComputable ModelsSYS 02 / EXPLORATIONDesign Space ExplorationModels · Conditions · ObjectivesBetter SolutionsSYS 03 / INTELLIGENCEEngineering AgentsProblems · Context · KnowledgeExecutable Tasks
01 | PLATFORM ARCHITECTURE

Model, explore, and execute on one platform

Connect problem framing, modeling, exploration, execution, and validation in one engineering workflow.

UNIFIED ENGINEERING AI PLATFORM

Unified Engineering AI Platform

Shared services, governance, and evidence connect every system.

Governance, Validation & Traceability
01Boundary ControlModel limits · Access control02Execution ControlAuthorization boundaries · End-to-end audit03ValidationV&V · Credibility assessment04Evidence TraceVersion trace · Engineering evidence
SYS 01/Integrated Modeling

Integrated Modeling

INTEGRATED MODELING

Turns real engineering objects into models that engineers can solve and validate

INPUTObjects·Physics·Data·Constraints
COREHybrid Engineering Solving Engine
OUTPUTComputable models·Validation baselines
SYS 02/Design Space Exploration

Design Space Exploration

DESIGN SPACE EXPLORATION

Compares options across objectives, variables, conditions, and constraints

INPUTComputable models·Variables·Conditions·Objectives
COREScalable Design Exploration Engine
OUTPUTBetter solutions·Validated results
SYS 03/Engineering Agents

Engineering Agents

ENGINEERING AGENTS

Uses engineering context and knowledge to plan work and retain validated learning

INPUTGoals·Context·Tools·Knowledge
COREEngineering Models & Agents
OUTPUTExecutable tasks·Engineering learning
ORCHESTRATION BUSEngineering agents coordinate modeling and exploration within approved boundaries, then return validated results to the task context.
Shared Platform Services
Runtime & Orchestration
Engineering TasksEngineering ContextCapability Registry
Trust & Governance
Identity & AccessEngineering EvidenceVersion Control
Engineer-Led Workflow
STEP 01Define the ProblemObjects, conditions, goals, and constraints
STEP 02Build the ModelTurn real objects into computable models
STEP 03Explore OptionsCompare and improve candidate solutions
STEP 04Validate the ResultVerify results and build engineering evidence
STEP 05Build Reusable CapabilityReuse methods, insights, and applicability boundaries
02 | INTEGRATED MODELING

Model Real Systems with the Right Physics, Data, and AI

Combine mechanistic, data-driven, and hybrid methods to balance fidelity, speed, and cost.

INTEGRATED MODELING

Integrated Modeling

Engineering objects, physics, data, constraints, and validation baselines in one system.

INPUTObjects · Physics · Data · ConstraintsFrom real engineering and upstream systems
SYSTEM ROLEModel · Solve · Check · ValidateHybrid Engineering Solving Engine: Turns real engineering objects into models that engineers can solve and validate
OUTPUTComputable models · Validation baselinesDelivered downstream for engineering review

MODELING MECHANISM

Hybrid Engineering Solving Engine

HYBRID ENGINEERING SOLVING ENGINE

Select the modeling path that fits the physics, data, fidelity target, and validation criteria.

01

Define Objects

objects, geometry, physics, scale

02

Set Constraints

boundaries, fidelity, data, acceptance

03

Choose a Paradigm

mechanistic | data-driven | hybrid

04

Model and Solve

models, tasks, solvers, compute

05

Validate and Capture

error, boundaries, baselines

01/Mechanistic Modeling

Mechanistic Modeling

Governing equations and multiphysics

Build computable models and validation baselines from governing equations, materials, and engineering boundaries FEA, CFD, Hydrodynamics, Multiphysics Physics-grounded | Controlled accuracy | Proven standards Solver execution, Standard checks, Validation baselines

Modeling Methods FEACFDHydrodynamicsMultiphysics
Validation Loop Solver executionStandard checksValidation baselines

02/Data-Driven Modeling

Data-Driven Modeling

Engineering data and model learning

Learn mappings, operators, and state evolution from engineering data and validate the resulting models ROM, Surrogates, Operator Learning, Data Assimilation Fast response | Broad exploration | Continuous learning Training and inference, Versioned data, Model checks

Modeling Methods ROMSurrogatesOperator LearningData Assimilation
Validation Loop Training and inferenceVersioned dataModel checks

03/Hybrid Modeling

Hybrid Modeling

Physics, data, and optimization together

Combine mechanistic models, learned models, and optimization strategies into one governed modeling path Physics + data, Multi-fidelity, Adaptive solving, AI-assisted solving Complementary methods | Governed boundaries | Reusable strategy Paradigm orchestration, Consistency checks, Validation feedback

Modeling Methods Physics + dataMulti-fidelityAdaptive solvingAI-assisted solving
Validation Loop Paradigm orchestrationConsistency checksValidation feedback

Integrated Modeling Value

01Computable Models
02Fit-for-Purpose Methods
03Verified Accuracy
04Reusable Strategies
03 | DESIGN SPACE EXPLORATION

Explore More Options and Converge Faster

Use HPC, DOE, DSE, and MDO to evaluate variables, conditions, objectives, and constraints at scale.

DESIGN SPACE EXPLORATION

Design Space Exploration

Design spaces, exploration strategies, scalable execution, and validation in one system.

INPUTComputable models · Variables · Conditions · ObjectivesFrom real engineering and upstream systems
SYSTEM ROLEOrchestrate · Schedule · Execute · GovernScalable Design Exploration Engine: Compares options across objectives, variables, conditions, and constraints
OUTPUTBetter solutions · Validated resultsDelivered downstream for engineering review

Computable Model

1

From integrated modeling

EXPLORATION MECHANISM

Scalable Engineering Exploration Kernel

SCALABLE ENGINEERING EXPLORATION KERNEL

1 computable model → N candidates → continuous convergence

Expand candidates across the engineering space, execute at scale, and converge through validation

Better Solutions

N

Validated results | applicability

Engineering Space
Exploration Orchestration
Execution at Scale
Validation Convergence
EXPLORATION FOUNDATION

Runtime Environments

Assemble the software and tools each task needs

CAE and SolversContainers and DependenciesLicenses and Access

Compute Resources

Match each task to the right compute

CPU and HPCGPU and AICloud and Hybrid Compute

Data Context

Bind the engineering and live data each run needs

Engineering DataSensor StreamsLive System State

Design Exploration Outcomes

01Space Explored
02Options Compared
03Exploration Recovered
04Results Validated
04 | ENGINEERING AGENTS

Turn Engineering Goals into Executable Work

Understand the context, connect the right tools, and retain what each validated task teaches.

ENGINEERING AGENTS

Engineering Agents

Engineering context, reasoning, planning, and reusable learning in one system.

INPUTGoals · Context · Tools · KnowledgeFrom real engineering and upstream systems
SYSTEM ROLEUnderstand · Reason · Plan · LearnEngineering Models & Agents: Uses engineering context and knowledge to plan work and retain validated learning
OUTPUTExecutable tasks · Engineering learningDelivered downstream for engineering review

CORE TECHNOLOGY

Engineering Models & Agents

ENGINEERING MODELS & AGENTS

Ground plans in engineering objects, constraints, and validated knowledge.

01/Engineering Input

ENGINEERING CONTEXT UNDERSTANDING

Engineering Context

Ground the task in objects, physics, conditions, data, goals, and constraints

ObjectsPhysicsOperating ConditionsDataGoalsConstraints
02/Reasoning Kernel

ENGINEERING REASONING AND PLANNING

Engineering Reasoning and Planning

Break down goals, reason over constraints, and build an executable plan

Task BreakdownMethod SelectionTool MatchingConstraint ReasoningExecution PlanAcceptance Criteria
03/Task Loop

TASK EXECUTION AND LEARNING

Task Execution and Learning

Invoke modeling, exploration, and engineering tools to advance tasks, validate results, and retain learning

Integrated ModelingDesign Space ExplorationEngineering ToolsTask ExecutionResult ValidationLearning Capture

Engineering Agent Outcomes

01Tasks Executable
02Process Traceable
03Results Reviewed
04Experience Reused
05 | GOVERNANCE, VALIDATION & TRACEABILITY

Keep Models, Runs, and Results Ready for Review

Governance, validation, and traceability connect model versions, compute environments, approvals, and review evidence across the platform.

GOVERNANCE, VALIDATION & TRACEABILITY

Governance, Validation & Traceability

Make every model, run, and result traceable, reproducible, and auditable.

Model Trust

Verify model boundaries, versions, fidelity, and applicability

Compute Trust

Reproduce environments, resources, methods, and compute processes

Execution Trust

Govern tasks, access, human approvals, and capability calls

Result Trust

Audit result checks, evidence chains, and accountability

Cross-layer CoverageCROSS-LAYER COVERAGE
Integrated ModelingDesign Space ExplorationEngineering AgentsGEWU / LUBAN / MOZISolutions & Industry Workflows

Trusted Task Chain

TRUSTED TASK CHAIN

Governed Inputs01

objects | sources | acceptance criteria

Governed Methods02

versions | methods | applicability

Execution Control03

resource calls | approvals | run records

Result Verification04

replay | comparison | confidence

Evidence Archive05

versions | delivery evidence | accountability

Trusted Validation & Engineering EvidenceTRUSTED VALIDATION & ENGINEERING EVIDENCE
V&VBenchmark ValidationCORA CorrelationAudit LogsEngineering Evidence
06 | ENGINEERING AI VALUE PATHS

Put Platform Capabilities to Work

Use products and solutions for proven methods. Build new workflows on the platform while the method is still evolving.

01 / PROVEN CAPABILITIES

Proven Capabilities

Delivered through Products and Solutions

Deploy validated methods through GEWU, LUBAN, MOZI, and standard solutions.

Standard ProductsStandard SolutionsValidation Evidence
Explore Products
02 / NEW OR EVOLVING CAPABILITIES

New or Evolving Capabilities

Built on the Engineering AI Platform

Start with the engineering problem and validation target. Connect the tasks, tools, and controls needed to build and prove the workflow.

Problem DefinitionCapability OrchestrationPilot Validation
Explore Solutions

YUANSUAN / ENGINEERING AI VISION

Engineering AIThe Intelligent Foundationof Modern Engineering

Give engineers the models, compute, and intelligence to solve more complex problems with confidence.