German Research Centre for Artificial Intelligence

Efficient Chebyshev reconstruction for the anisotropic equilibrium model in magnetic particle imaging

Abstract Objective: Magnetic Particle Imaging (MPI) is a tomographic imaging modality capable of real-time, high-sensitivity mapping of superparamagnetic iron oxide nanoparticles. Model-based image reconstruction provides an alternative to conventional methods that rely on a meas...

LLM-driven design of physics-constrained constitutive models: Two agents are better than one

Abstract Developing constitutive models that capture how materials deform under load traditionally requires years of specialized expertise in continuum mechanics, machine learning, and scientific programming. Large language models (LLMs) have recently been shown to lower this bar...

Requirements for numeric models as sources of synthetic data for predicting real-world data sets in progressive deep drawing processes

Abstract In the field of forming technology, synthetic data generated by finite element (FE) simulations is increasingly being used to train machine learning (ML) models for part quality prediction. However, the predictive accuracy on real-world process data is often limited by t...

In-Context Molecular Property Prediction with LLMs: A Blinding Study on Memorization and Knowledge Conflicts

Abstract The capabilities of large language models (LLMs) have expanded beyond natural language processing to scientific prediction tasks, including molecular property prediction. However, their effectiveness in in-context learning remains ambiguous, particularly given the potent...

Anticipating the hospital of the future: stakeholder perspectives on 6G technology requirements in health care

PURPOSE: The ongoing 6G standardization process is largely driven by technical advancements, but with limited consideration of sector-specific needs, particularly in health care. The current scientific literature primarily focuses on technical aspects, offering minimal empirical...

Amortized inverse kinematics via graph attention for real-time human avatar animation

Inverse kinematics (IK) is a core operation in animation, robotics, and biomechanics: given Cartesian constraints, recover joint rotations under a known kinematic tree. In many real-time human avatar pipelines, the available signal per frame is a sparse set of tracked 3D joint po...

BAASNet: boundary-aware deep learning for accurate polyp segmentation in colonoscopy

Abstract Colorectal polyps are primarily detected through colonoscopy, which plays a central role in early cancer prevention. Precise polyp segmentation supports treatment planning and diagnostic accuracy by providing masks that encode clinically relevant structures. Recent advan...

Novel class identification: A human‐in‐the‐loop approach for efficient species recognition in bioacoustic data

Abstract Passive acoustic monitoring (PAM) is widely used to collect large‐scale wildlife data encompassing many species, but efficiently identifying all species—particularly rare and potentially endangered ones—without exhaustive manual examination of the entire dataset remains...