Scientific ML & Digital Twin
PINNs, thermal modeling, surrogate models, and engineering validation.
Founded in 2022, Semantic Graph develops Industrial Physical AI products that combine Physics AI, industrial vision, Knowledge Graph, and GraphRAG for semiconductor and advanced manufacturing quality and process intelligence.
Our mission is to help engineers make faster, evidence-grounded decisions by connecting physical behavior with manufacturing knowledge.
We communicate the delivery capabilities required for industrial AI without overexposing individual personal information.
PINNs, thermal modeling, surrogate models, and engineering validation.
AI vision, SWIR, multimodal inspection, model evaluation, and error analysis.
Quality ontology, Knowledge Graph, explainable RCA, and engineering agents.
Quality criteria, process context, KPIs, on-premise GPU, and enterprise integration.
NVIDIA GPU/CUDA, planned PhysicsNeMo evaluation, academic context around the NVIDIA DLI Science and Engineering Teaching Kit, x86 Physical AI Servers, and selective Jetson edge infrastructure are technology ecosystem references.
Only contracted or formally approved relationships are labeled as formal partners. Discussions involving Brown University/Professor Karniadakis are collaboration exploration and do not represent a formal joint-research agreement.
We review collaboration opportunities across Physics AI, semiconductor thermal engineering, AI vision, Knowledge Graph/GraphRAG, x86 Physical AI Servers, and edge/on-premise infrastructure.