Inverse Intelligence Group

We turn forward models upside down

If you cannot clearly assign the cause to measured data, then you are dealing with inverse problems. Our team of experts in physics and data science can help: Using invertible neural networks and scientific AI, we turn your measurement noise into understandable parameters and simulations into predictive sensors.

We make ambiguous measurement and process data from industrial plants finally analyzable - through the development and integration of AI solutions. Our open VIPR Inversion Engine delivers Bayesian inference/inversion, uncertainty quantification, virtual sensing, real-time alerting, and fully integrable REST interfaces – production-ready, transparent, and modularly extensible. We combine physical understanding, scientific machine learning, and productive implementation to help industrial companies and applied research institutions gain robust insights and control logic from the most complex data.

Inverse problems we have solved

Services

We develop complete AI-based analysis and process intelligence for industrial measurement systems:

Bayesian Inference & INNs

Development, training, and adaptation of invertible models for layer thicknesses, material parameters, plasma states, sensor drifts, and other ambiguous measurement processes.

Data Strategy & Data Acquisition

Planning and construction of data pipelines, data models, data collection, robustification, and automated data management.

Synthetic Data & Simulation

Generation of training data using physical models or generative models – ideal when data is scarce or expensive.

VIPR Integration

Implementation of a modular analysis and control pipeline based on our open-source VIPR framework. With REST API, plug-ins, alerting, and dashboards.

Product Integration

Seamless integration into existing systems, measurement device software, automation systems, or edge infrastructure.

Interested?

Our Working Method

Problem Definition and Domain Analysis
  • Formulation of the inverse or data-driven problem in physical context
  • Analysis of relevant mechanisms, boundary conditions, and measurement chains
  • Identification of reconstructable quantities and necessary diagnostics
Data Acquisition and Preliminary Analysis
  • Structured recording of measurement, process, and metadata
  • Synchronization, filtering, calibration, and quality checking
  • Extraction of physically significant features and time regimes
Design of Inversion Model and Training
  • Selection of suitable model architecture (e.g., Invertible Neural Network, Physics-informed Neural Network, Bayesian Inference)
  • Integration of physical constraints and process knowledge
  • Training with real, synthetic, or hybrid datasets, possibly on high-performance computers
System Integration and API Deployment
  • Provision of the model as a scalable API or edge component
  • Connection to measurement systems, controls, and data services
  • Secure operation via REST, gRPC, or OPC-UA
Validation and Pilot Operation
  • Experimental verification of reconstructions and predictions
  • Analysis of uncertainties, robustness, and real-time capability
  • Iterative adjustment based on pilot data and expert feedback
Production Deployment and Lifecycle Support
  • Deployment in target environment with monitoring and versioning
  • Continuous model monitoring, drift detection, and re-training
  • Technical support, documentation, and continuous optimization

We are more than just another AI agency

We are not just an AI agency on cloud nine. We are physicists, machine learning engineers, scientists, innovators, and we work in a practical manner. We combine science-driven AI with a deep understanding of processes and systems. With open frameworks like our VIPR Inversion Stack, Bayesian inference, physics-informed neural networks, and normalizing flows, we build transparent, auditable, and industry-ready pipelines that seamlessly link measurement data, simulations, and automation systems. This creates solutions that enable robust state reconstructions, secure process control, and scalable integrations, far beyond what traditional AI agencies deliver.

Dr. rer. nat. Nico Mothes
Lead ML Engineer | Plasma Physics
Dr. rer. nat. Nico Mothes
Profil about Dr. rer. nat. Nico Mothes
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Bernd Helm
ML Engineer | Software Architecture
Bernd Helm
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Dr. Sascha Creutzburg
ML Engineer | Physicist
Dr. Sascha Creutzburg
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What Distinguishes Us

Scientific Machine Learning

We develop science-driven AI models that explicitly consider physical laws, nonlinearities, and uncertainties. Our focus is on physics-informed neural networks, normalizing flows (INN/cINN), and hybrid models that combine data and process knowledge. This allows us to derive robust state reconstructions and process predictions from limited or noisy measurement data.

Bayesian Inference

For inverse problems, we use Bayesian inversion methods and invertible neural networks to determine latent states from observable effects – including uncertainty quantification. This approach allows proper treatment of ambiguous measurement situations, incorporation of prior knowledge, and statistically validated reconstruction of solution spaces. Our methods are applied where classical optimization fails or physical models are incomplete.

Understanding Complex Systems

Our team of physicists and computer scientists analyzes systems with many coupled mechanisms, nonlinear feedbacks, and emergent behavior. Whether plasma, coating, or measurement processes: we model the underlying structures in latent state space and make their dynamics quantifiable. This allows us to visualize stability limits, transition regimes, drift phenomena, and process hysteresis – and make them usable for data-driven optimization.

Open Source Frameworks

Our tools are based on open, transparent frameworks such as PyTorch or VIPR, deployed in large research facilities like MLZ. We develop modular pipelines for data acquisition, analysis, inversion, and visualization – fully traceable and extensible. Open source creates trust, facilitates audits, and enables long-term maintainability in industrial applications.

Measurement and Automation Technology

We integrate AI models into industrial measurement and automation systems. This includes:

  • Multi-sensor fusion
  • Real-time state reconstruction
  • REST and edge deployments
  • Feedback to control systems

We connect modern AI with proven measurement technology and automation to make processes more robust, adaptive, and predictable.

Ready to take the first step?

Contact us now and we will find the right solution for your requirements!