Publications related to Game physics

Improvements to the dynamic wake meandering model by incorporating the turbulent Schmidt number

Fernando Porté Agel, Peter Andreas Brugger, Corey Dean Markfort

Predictions of the dynamic wake meandering model (DWMM) were compared to flow measurements of a scanning Doppler lidar mounted on the nacelle of a utility-scale wind turbine. We observed that the wake meandering strength of the DWMM agrees better with the ...

2024

BamX: Rethinking Deployability in Architecture through Weaving

Mark Pauly, Stefana Parascho, Seiichi Eduardo Suzuki Erazo, Yingying Ren, Tian Chen

Deployable gridshells are a class of planar-to-spatial structures that achievea 3D curved geometry by inducing bending on a flat grid of elastic beams. However, theslender nature of these beams often conflicts with the structure’s load-bearing capacity.To ...

De Gruyter2023

Synthetic Contrasts in Musculoskeletal MRI A Review

Tobias Kober, Tom Hilbert

This review summarizes the existing techniques and methods used to generate synthetic contrasts from magnetic resonance imaging data focusing on musculoskeletal magnetic resonance imaging. To that end, the different approaches were categorized into 3 diffe ...

LIPPINCOTT WILLIAMS & WILKINS2023

"A physics-based model for wind turbine wake expansion in the atmospheric boundary layer"

Fernando Porté Agel, Dara Vahidi

Vahidi, Dara, and Fernando Porté-Agel. "A physics-based model for wind turbine wake expansion in the atmospheric boundary layer." Journal of Fluid Mechanics 943 (2022). ...

Zenodo2022

Synthesis and Analysis of 3D shapes with Geometric Deep Learning in Computer-Aided Engineering

Edoardo Remelli

In this thesis, we advocate that Computer-Aided Engineering could benefit from a Geometric Deep Learning revolution, similarly to the way that Deep Learning revolutionized Computer Vision. To do so, we consider a variety of Computer-Aided Engineering pr ...

EPFL2022

Learning to Simulate Realistic LiDARs

Pascal Fua, Benoît Alain René Guillard

Simulating realistic sensors is a challenging part in data generation for autonomous systems, often involving carefully handcrafted sensor design, scene properties, and physics modeling. To alleviate this, we introduce a pipeline for data-driven simulation ...

2022

The spectrum of early career physics

Tara Nanut

There isn't one single 'early career experience' in physics, and different subfields involve very different opportunities and challenges. Seven early career physicists who work on a range of research topics in different subfields discuss their views on the ...

NATURE PORTFOLIO2021

Early Prediction of Conceptual Understanding in Interactive Simulations

Jade Maï L Cock, Tanja Christina Käser Jacober, Christian Giang, Mirko Marras

Interactive simulations allow students to independently ex- plore scientific phenomena and ideally infer the underlying principles through their exploration. Effectively using such environments is challenging for many students and there- fore, adaptive gui ...

2021

Model-Class Selection Using Clustering and Classification for Structural Identification and Prediction

Ian Smith, Sai Ganesh Sarvotham Pai, Masoud Sanayei

Structural identification using physics-based models and subsequent prediction have much potential to enhance civil infrastructure asset-management decision-making. Interpreting monitoring information in the presence of multiple uncertainty sources and sys ...

2021

From the Principle of Inertia to the Death Drive: The Influence of the Second Law of Thermodynamics on the Freudian Theory of the Psychical Apparatus

Pierre Magistretti, Jean-Philippe Ansermet

In the Freudian theory of the psychical apparatus, the introduction from the 1920s onward of the second drive dualism appears as a major turning point. The idea of a "death drive," first expressed in Beyond the Pleasure Principle (Freud, 1920), is generall ...

Frontiers Media2020

Representations and descriptors unifying the study of molecular and bulk systems

Kevin Rossi

Establishing a unified framework for describing the structures of molecular and periodic systems is a long-standing challenge in physics, chemistry, and material science. With the rise of machine learning methods in these fields, there is a growing need fo ...

WILEY2019

GarNet: A Two-Stream Network for Fast and Accurate 3D Cloth Draping

Pascal Fua, Mathieu Salzmann, Victor Constantin, Erhan Gündogdu

While Physics-Based Simulation (PBS) can accurately drape a 3D garment on a 3D body, it remains too costly for real-time applications, such as virtual try-on. By contrast, inference in a deep network, requiring a single forward pass, is much faster. Taking ...

IEEE2019

Sim-to-real transfer reinforcement learning for control of thermal effects of an atmospheric pressure plasma jet

Berend Smit, Matthew David Witman

Applications of atmospheric pressure plasma jets (APPJs) present challenging feedback control problems due to the complexity of the plasma-substrate interactions. The plasma treatment of complex substrates is particularly sensitive to changes in the physic ...

2019

Learning new physics from a machine

Raffaele Tito D'Agnolo, Andrea Wulzer

We propose using neural networks to detect data departures from a given reference model, with no prior bias on the nature of the new physics responsible for the discrepancy. The virtues of neural networks as unbiased function approximants make them particu ...

American Physical Society2019

Fast and ultrafast electron microscopies for skyrmions and plasmonics

Gabriele Berruto

In recent years, topology gained a central role in physics. We learnt that energetics could be often explained better by classes of objects defined by having qualitative differences. In today's jargon, we say they are topologically distinct. The process of ...

EPFL2019

Physics-based Reconstruction and Animation of Humans

Alexandru Eugen Ichim

Creating digital representations of humans is of utmost importance for applications ranging from entertainment (video games, movies) to human-computer interaction and even psychiatrical treatments. What makes building credible digital doubles difficult is ...

EPFL2017

Reconstruction and visualization of large-scale volumetric models of neocortical circuits for physically-plausible in silico optical studies

Henry Markram, Felix Schürmann, Marwan Muhammad Ahmed Abdellah, Juan Bautista Hernando Vieites, Stefan Eilemann, Nicolas Antille

Background: We present a software workflow capable of building large scale, highly detailed and realistic volumetric models of neocortical circuits from the morphological skeletons of their digitally reconstructed neurons. The limitations of the existing a ...

2017

Reconstructing Personalized Anatomical Models for Physics-based Body Animation

Ladislav Kavan, Alexandru Eugen Ichim

We present a method to create personalized anatomical models ready for physics-based animation, using only a set of 3D surface scans. We start by building a template anatomical model of an average male which supports deformations due to both 1) subject-spe ...

Assoc Computing Machinery2016

Multiphysics Computational Modeling in C-Heart

Simone Deparis

From basic science to translation, modern biomedical research demands computational models which integrate several interacting physical systems. This paper describes the infrastructural framework for generic multiphysics integration implemented in the soft ...

Siam Publications2016

Building and Animating User-Specific Volumetric Face Rigs

Mark Pauly, Ladislav Kavan, Alexandru Eugen Ichim, Merlin Eléazar Nimier-David

Currently, the two main approaches to realistic facial animation are 1) blendshape models and 2) physics-based simulation. Blendshapes are fast and directly controllable, but it is not easy to incorporate features such as dynamics, collision resolution, or ...

The Eurographics Association2016