VIPR Framework

The Problem: Ambiguity, Time Consumption and Heterogeneous Use Cases

In neutron reflectometry, central material properties must be reconstructed from noisy reflectivity measurement data. These include, for example, the layer thickness of thin films in the range of a few to hundreds of Angstroms, the roughness of interfaces, density profiles in the form of scattering length density, and magnetic properties in polarized measurements.

Classical analysis is time-intensive, often occurring hours or even days after the experiment, and frequently required additional manual adjustments by experts. For real-time feedback during experiments, automated adjustment of measurement parameters, high-throughput analyses with thousands of datasets, or in-situ process control, this is simply not suitable.

Added to this is the fundamental ambiguity of inverse problems. Different material configurations can generate identical measurement signals. Without deep expert knowledge and physical understanding, reliable interpretation is nearly impossible.

The greatest challenge for a software framework in general often lies in the heterogeneity of use cases. VIPR - as a software framework for AI workflows - should be usable not only for neutron reflectometry, but also for GIWAXS analyses, ptychography, plasma diagnostics in coating processes, and potentially even for spectroscopy, particle physics, and many other application areas.

The target groups are initially researchers from physics and materials science, and NOT computer scientists. This user group wants to solve their specific, sometimes highly specialized problems with manageable adjustments. This creates a direct tension with the complexity and power that a universal software framework should offer.

Our Solution: Modularity Meets Power

The basic idea behind the VIPR framework is to easily replace, adapt, or extend individual components in a workflow. The framework is based on a modular concept with multiple abstraction levels, designed to offer both flexibility for experts and easy usability for beginners.

Hooks, Filters and Plugins for Maximum Flexibility

Every execution step in the workflow can be made visible and loggable through hooks. Hooks are called before and after each step execution and enable debugging outputs and logs, visualization generation, or intermediate result storage without intervention in the core logic.

With filters, input and output values can be specifically modified before each processing step. This enables normalization, data augmentation, or transformation between different representations.

Plugins are the central extension concept. They enable not only the integration of corresponding hooks and filters for basic steps, but also the complete replacement of workflow steps with custom logic. A plugin can bring its own pre-trained AI models, implement domain-specific data pre- and post-processing, or even introduce entirely new, more complex workflow types.

Invertible Neural Networks as Methodological Core

Unlike classical neural networks, Invertible Neural Networks learn a bidirectional mapping. They can both compute from material parameters to measurement data, thus solving the forward problem, and conversely infer the underlying material parameters from the measurement signal.

Functional principle of invertible neural networks: Bidirectional mapping between material parameters and measurement data enables solving both forward and inverse problems.

The decisive advantage is that the analysis occurs in a single forward pass through the network. Time-consuming iterative optimization is eliminated. Moreover, INNs can predict not only individual parameter sets, but complete posterior probability distributions. This quantifies the uncertainty of reconstruction and makes multiple possible solutions of an ambiguous inverse problem explicitly visible.

The results of INN prediction can also be used as initial conditions for classical local fitting, which further increases robustness and enables dramatic accelerations of analysis time.

The VIPR framework enables the reconstruction of probability distributions for inverse problems. The following visualizations show typical analysis results from neutron reflectometry and other applications, where invertible neural networks transform ambiguous measurement data into interpretable parameter distributions.

Handling Ambiguity and Physical Plausibility

Inverse problems by definition often have multiple valid solutions. The deployed INNs quantify this uncertainty by predicting complete probability distributions instead of individual values. At the same time, it had to be ensured that the predicted parameters are physically meaningful. For this purpose, special constraints and physics-informed loss functions were implemented that enforce physical laws during training.

Balancing Act between Power and User-Friendliness

The fundamental tension between the power of a universal software framework and simplicity for the user runs through all design decisions. The solution lies in several parallel access paths. Researchers who need quick results can work through the web interface with preconfigured workflows. Those who want to dive deeper can work through the config system and recombine existing components. For highly specialized applications, the full plugin mechanism is available.

It is crucial that high-quality example implementations and documentation are provided. These then show best practices, demonstrate the power of the framework, and simultaneously serve as starting points and blueprints for custom adaptations. This minimizes the barrier to entry for solving concrete problems without unnecessarily restricting flexibility.