Control what your model experiences.
Vary environments, materials, inputs and actions. Explore the operating envelope through controlled simulation campaigns.
The platform
The simulation layer
for Physical AI.
Generate the experience world models learn from.
Test their predictions and decisions against simulated outcomes.
Use the simulation software your work requires. Laplace handles configuration and runs scenarios in parallel on large-scale cloud compute, producing data for training and testing Physical AI.
For Physical AI and world-model teams
Control the experience.
Challenge the model.
Learn from what sensors cannot see.
Pair simulated observations with internal fields and state histories, creating supervision for models that infer hidden conditions.
Find where predictions and decisions break.
Use held-out scenarios to compare forecasts and control strategies against reference simulations. Expose failure modes and tradeoffs.
Control the experiment.
Scale the execution.
No HPC infrastructure to configure or manage. Define the experiment; Laplace handles the execution.
Bring your simulation software.
You define the system, conditions and outputs. Laplace handles the simulator configuration.
Vary the conditions.
Specify the parameters and regimes to explore, including cases reserved for evaluation.
Scale the compute.
Laplace prepares cloud resources, runs independent cases in parallel and identifies failed runs.
Inspect the outputs.
Receive results tied to case inputs and simulation settings, with recorded checks and limitations.
Fidelity depends on the solver, numerical resolution and physical assumptions. Validation criteria are defined for each campaign.
Initial demonstrations
Physical states.
Decisions. Consequences.
Explore the methods, outputs and limits behind the work.
Reveal what wall sensors miss.
Internal temperature fields paired with wall-sensor histories provide training data for inferring hidden reactor conditions.
Data generation demonstrated. Learned sensing and control remain pending.
Explore the case study ↗Make the cost of a decision visible.
Simulated radio coverage supports route comparison, exposing the tradeoff between connectivity and flight distance.
Demonstrated in simulation. Some signal loss remains; field validation is still needed.
Explore the case study ↗Challenge a learned skill with unfamiliar physics.
A learned grip and a fixed plan face the same unfamiliar virtual tissues. The comparison reveals both improved performance and remaining failures.
Simulation results only. No real tissue or clinical validation.
Explore the case study ↗Research Hub
Follow the process.
See what holds up.
From a difficult question to a simulated world, a comparison and a result worth questioning.
Explore the simulation ↗
Robotics · touch and control
Robotic finger design simulation
Simulate robotic fingers before fabrication. Compare skins, touch sensors and controllers across manipulation tasks.
Explore the simulation ↗Explore the simulation ↗
Energy · connected-system resilience
Large-scale cyberattack simulation
Simulate cyber disruption and physical failures at a fictional energy company. Compare how defensive responses affect electricity delivery and business operations.
Explore the simulation ↗Explore the simulation ↗
Energy · finding the cause of a fault
Reactor fault diagnosis simulation
Create hidden reactor faults with similar readings, then test whether an intervention, a longer wait or another measurement reveals the cause.
Explore the simulation ↗Explore the simulation ↗
Energy · testing diagnostic reliability
Reactor diagnosis stress-test simulation
Stress-test reactor diagnosis across concurrent faults, uncertain plant conditions and different physical models.
Explore the simulation ↗Early access
Prove it on your workload.
Bring the simulator, conditions and outputs you need. Scope a campaign and measure it against your current workflow.
Agree solver support, the execution budget and acceptance criteria before the campaign begins.
Join the waitlist ↗The benchmark is your workload.
- Time to usable results
- Completed and failed cases
- Engineering effort
- Cost per completed case
- Consistency with your reference