Digital twins for post-earthquake damage assessment of free-standing buildings including damage information

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Estimating ecosystem-atmosphere fluxes such as evapotranspiration (ET) in a robust manner and at a global scale remains a challenge. Methods based on machine learning (ML) have shown promising results in achieving such upscaling, providing a complementary ...

Monitoring of cardiac output (CO) is a mainstay of hemodynamic management in the acutely or critically ill patient. Invasive determination of CO using thermodilution, albeit regarded as the gold standard, is cumbersome and bears risks inherent to catheteri ...

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Faithful human performance capture and free-view rendering from sparse RGB observations is a long-standing problem in Vision and Graphics. The main challenges are the lack of observations and the inherent ambiguities of the setting, e.g. occlusions and dep ...

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Scientific Machine Learning (SciML) is a recently emerged research field which combines physics-based and data-driven models for the numerical approximation of differential problems. Physics-based models rely on the physical understanding of the problem at ...

Digital twins for post-earthquake damage assessment of free-standing buildings including damage information

Chat with Graph Search

On the added value of sequential deep learning for the upscaling of evapotranspiration

Machine learning-enabled estimation of cardiac output from peripheral waveforms is independent of blood pressure measurement location in an in silico population

Teaching Not To

Enhancing skin lesion classification: a CNN approach with human baseline comparison

Rising from rubble - Leveraging existing construction tools for upcycling concrete waste into slender walls

Neural Garment Modeling

Incorporating Projective Geometry into Deep Learning

A Study on Quantum Graph Neural Networks Applied to Molecular Physics

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Stereo Hand-Object Reconstruction for Human-to-Robot Handover

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Integer Linear Programming for Unsupervised Training Set Selection in Molecular Machine Learning

Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets

Retrieval of Surface Solar Radiation through Implicit Albedo Recovery from Temporal Context

Airborne sensor fusion: Expected accuracy and behavior of a concurrent adjustment

MetaCap: Meta-learning Priors from Multi-view Imagery for Sparse-View Human Performance Capture and Rendering

FLO:RE – A new floor system made of reused reinforced concrete and steel elements

Application of Deep Learning and Geospatial Analysis in Soil Loss Risk in the Moulouya Watershed, Morocco

Enabling Uncertainty Estimation in Iterative Neural Networks

Combining physics-based and data-driven models: advancing the frontiers of research with scientific machine learning