Scientific / physics-grounded LQMs
SandboxAQ
Large Quantitative Models grounded in physics, chemistry, biology and mathematics for scientific and quantitative problems.
Read comparison →Category research / 2026
State of Large Quantitative Models 2026
A source-linked market map of the emerging LQM and quantitative-AI landscape. The central observation: vendors are converging on quantitative intelligence from different starting points—science, physics, industrial simulation, revenue forecasting and horizontal model infrastructure.
Four routes into the category
- Scientific LQMs. SandboxAQ publicly centers LQMs on physics, chemistry, biology and mathematics.
- Physics AI. PhysicsX builds an engineering platform around simulation and Deep Physics Models.
- Industrial AI surrogates. Siemens PhysicsAI applies geometric deep learning to engineering simulation workflows.
- Domain LQMs. Aviso applies LQMs to revenue forecasting and pipeline intelligence.
The horizontal opening
LargeQuant is pursuing a different layer: the reusable infrastructure for model identity, quantitative contracts, benchmarking, evidence, governance and production operation across domains.
Physics AI / industrial engineering
PhysicsX
AI-native engineering software combining simulation, physics AI, data and engineering applications.
Read comparison →Industrial simulation / engineering AI
Siemens Simcenter PhysicsAI
Geometric deep learning and reduced-order AI models for accelerating engineering simulation and design exploration.
Read comparison →Revenue intelligence LQMs
Aviso
Large Quantitative Models used with language models and time-series data for forecasting, pipeline risk and revenue execution.
Read comparison →