Publications related to Physics-informed neural network estimation of material properties in soft tissue nonlinear biomechanical models

Related publications (19)

Mitigating imaging systematics for DESI 2024 emission Line Galaxies and beyond

Emission Line Galaxies (ELGs) are one of the main tracers that the Dark Energy Spectroscopic Instrument (DESI) uses to probe the universe. However, they are afflicted by strong spurious correlations between target density and observing conditions known as ...

IOP Publishing2025

Unsupervised Multi-view UAV Image Geo-localization via Iterative Rendering

Chang Xu, Li Mi

Unmanned Aerial Vehicle (UAV) Cross-View Geo-Localization (CVGL) poses significant challenges due to the substantial view discrepancies between oblique UAV images and overhead satellite images. Existing methods heavily rely on supervised learning with labe ...

2025

Abstract B037: AI-powered virtual tissues from spatial proteomics for clinical diagnostics and biomedical discovery

Eeshaan Jain, Johann Clemens Werner Wenckstern, Charlotte Cäcilie Gertrud Bunne

Spatial proteomics technologies have transformed our understanding of complex tissue architectures by enabling simultaneous analysis of multiple molecular markers and their spatial organization. The high dimensionality of these data, varying marker combina ...

American Association for Cancer Research (AACR)2025

Reconstruction of quantum states by applying an analytical optimization model

Pratyay Ghosh

When working with quantum states, analysis of the final quantum state generated through probabilistic measurements is essential. This analysis is typically conducted by constructing the density matrix from either partial or full tomography measurements of ...

2025

Stabilization of nonlinear systems by neural Lyapunov approximators and Sontag's formula

Adriano Mele

This note describes a method to train a Neural Network so that it approximates a control Lyapunov function for a nonlinear system in affine form. The network is trained in a physics-informed fashion, as the training data are generated by enforcing the nega ...

Institute of Electrical and Electronics Engineers Inc.2024

Where the White Continent Is Blue: Deep Learning Locates Bare Ice in Antarctica

Devis Tuia, Marc Conrad Russwurm

In some areas of Antarctica, blue-colored bare ice is exposed at the surface. These blue ice areas (BIAs) can trap meteorites or old ice and are vital for understanding the climatic history. By combining multi-sensor remote sensing data (MODIS, RADARSAT-2, ...

Amer Geophysical Union2024

Uncertainty quantification by direct propagation of shallow ensembles

Michele Ceriotti, Matthias Linus Kellner

Statistical learning algorithms provide a generally-applicable framework to sidestep time-consuming experiments, or accurate physics-based modeling, but they introduce a further source of error on top of the intrinsic limitations of the experimental or the ...

2024

Cross-transfer Knowledge between Speech and Text Encoders to Evaluate Customer Satisfaction

Tilak Purohit

Customer Satisfaction (CS) in call centers influences customer loyalty and the company's reputation. Traditionally, CS evaluations were conducted manually or with classical machine learning algorithms; however, advancements in deep learning have led to aut ...

International Speech Communication Association2024

Universal Neural-Cracking-Machines: Self-Configurable Password Models from Auxiliary Data

Carmela González Troncoso, Dario Pasquini

We introduce the concept of "universal"password model - a password model that, once pre-trained, can automatically adapt its guessing strategy based on the target system. To achieve this, the model does not need to access any plaintext passwords from the t ...

Institute of Electrical and Electronics Engineers Inc.2024

Empirical Sample Complexity of Neural Network Mixed State Reconstruction

Giuseppe Carleo, Filippo Vicentini

Quantum state reconstruction using Neural Quantum States has been proposed as a viable tool to reduce quantum shot complexity in practical applications, and its advantage over competing techniques has been shown in numerical experiments focusing mainly on ...

Verein Forderung Open Access Publizierens Quantenwissenschaf2024

Seismic Testing of Adjacent Interacting Masonry Structures – Analysis of a Blind Prediction

Katrin Beyer, Igor Tomic

Masonry aggregates, which emerged as layouts of cities and villages became denser, make up historical centres all over the world. In these aggregates, neighbouring structures may share structural walls that are joined at the interfaces by mortar or interlo ...

Springer2024

Deep learning-based recommendation system for metal-organic frameworks (MOFs)

Berend Smit, Kevin Maik Jablonka, Xiaoqi Zhang

This work presents a recommendation system for metal-organic frameworks (MOFs) inspired by online content platforms. By leveraging the unsupervised Doc2Vec model trained on document-structured intrinsic MOF characteristics, the model embeds MOFs into a hig ...

Royal Soc Chemistry2024

3D reconstruction of curvilinear one-dimensional objects viewed in transmission electron microscopy

Gulnaz Ganeeva

To obtain a more complete understanding of material microstructure at the nanoscale and to gain profound insights into their properties, there is a growing need for more efficient and precise methods that can streamline the process of 3D imaging using a tr ...

EPFL2023

Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes

Silvestro Micera, Simone Romeni

Objective. Peripheral nerve interfaces have the potential to restore sensory, motor, and visceral functions. In particular, intraneural interfaces allow targeting deep neural structures with high selectivity, even if their performance strongly depends upon ...

IOP Publishing Ltd2023

Regularization Techniques for Low-Resource Machine Translation

Alejandro Ramírez Atrio

Neural machine translation (MT) and text generation have recently reached very high levels of quality. However, both areas share a problem: in order to reach these levels, they require massive amounts of data. When this is not present, they lack generaliza ...

EPFL2023

Training Traffic Light Behavior with End-to-End Learning

Alexandre Massoud Alahi

In this work, we study neural network architectures that will reduce the number of infractions made by autonomous-driving agents. These agents control vehicles by providing future waypoints directly from a forward-facing camera. Building on top of the teac ...

Springer2023

Spatial-Temporal Graph Attention Fuser for Calibration in IoT Air Pollution Monitoring Systems

Olga Fink, Mengjie Zhao, Keivan Faghih Niresi

The use of Internet of Things (IoT) sensors for air pollution monitoring has significantly increased, resulting in the deployment of low-cost sensors. Despite this advancement, accurately calibrating these sensors in uncontrolled environmental conditions r ...

Institute of Electrical and Electronics Engineers2023

Finite Callan-Symanzik renormalisation for multiple scalar fields

Mikhail Chapochnikov, Sander Johannes Nicolaas Mooij

We study a finite, divergence-free approach to renormalisation originally proposed in the early '70s by Blaer and Young, and Callan. It is based on equations similar to the Callan-Symanzik equations and introduced in the context of the & lambda;& phi;4 the ...

ELSEVIER2023

Optimization and propagation of uncertainties of a THM numerical model representing a nuclear waste repository concept

Lyesse Laloui, Matthias Timothee Stanislas Wojnarowicz

Nuclear waste repositories are being considered by many countries to be the best solution to deal with the increasing amount of High-Level Waste (HLW) from nuclear power plant operation and decommissioning. The Swiss repository concept has been assessed si ...

Argo-E Group2023