SPINN: Advancing Cosmological Simulations of Fuzzy Dark Matter with Physics Informed Neural Networks

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Context. The Euclid mission of the European Space Agency will deliver weak gravitational lensing and galaxy clustering surveys that can be used to constrain the standard cosmological model and extensions thereof. Aims. We present forecasts from the combina ...

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 ...

We report the discovery of a complete Einstein ring around the elliptical galaxy NGC 6505, at z = 0.042. This is the first strong gravitational lens discovered in Euclid and the first in an NGC object from any survey. The combination of the low redshift of ...

The digitization of 3D deformable objects remains a significant challenge in computer graphics and vision, particularly in the accurate modeling of garments. Garments exhibit complex shape variability, non-rigid deformations, and frequent self-occlusion, m ...

Object detection, a fundamental task in computer vision, is crucial for various intelligent edge computing applications. However, object detection algorithms are usually heavy in computation, hindering their deployments on resource-constrained edge devices ...

We present CAPERS-LRD-z9, a little red dot (LRD) that we confirm to be a z = 9.288 broad-line active galactic nucleus (BLAGN). First identified as a high-redshift LRD candidate from PRIMER NIRCam photometry, follow-up NIRSpec/PRISM spectroscopy of CAPERS-L ...

The Birman-Williams theorem gives a connection between the collection of unstable periodic orbits (UPOs) contained within a chaotic attractor and the topology of that attractor, for three-dimensional systems. In certain cases, the fractal dimension of a ch ...

This study integrates deep learning and geospatial analysis to enhance soil loss estimation in the Moulouya Watershed, a region prone to erosion due to diverse topography and climatic conditions. Traditional models like the Universal Soil Loss Equation (US ...

The optimistic gradient method is useful in addressing minimax optimization problems. Motivated by the observation that the conventional stochastic version suffers from the need for a large batch size on the order of O(ϵ-2) to achieve an ϵ-stationary solut ...

More attention has been paid to integrating existing knowledge with data to understand the complex mechanical behaviour of geomaterials, but it incurs scepticism and criticism on its generalizability and robustness. Moreover, a common mistake in current da ...

We study how light scalar fields can change the stellar landscape by triggering a new phase of nuclear matter. Scalars coupled to nucleons can develop a non-trivial expectation value at finite baryon density. This sourcing of a scalar reduces the nucleon m ...

The annihilation of dark-matter particles may lead to the production of monochromatic gamma-ray emission. In this contribution, the search for spectral lines in the gamma-ray spectrum, using eight years of data collected with the space-borne Dark Matter Pa ...

The first two chapters develop ML-based models that generate conditional SDF estimates efficiently using conditioning information sets, resulting in minimal pricing errors and superior Sharpe ratios compared to traditional conditional asset pricing models. ...

BackgroundBronchiolitis Obliterans Syndrome (BOS), a fibrotic airway disease that may develop after lung transplantation, conventionally relies on pulmonary function tests (PFTs) for diagnosis due to limitations of CT imaging. Deep neural networks (DNNs) h ...

Context. The formation of stars is governed by the intricate interplay of nonideal magnetohydrodynamic (MHD) effects, gravity, and turbulence. Computational challenges have hindered a comprehensive 3D exploration of this interplay, posing a longstanding ch ...

Computational thermo-microstructural modelling has become a powerful tool for understanding the process- microstructure linkage in the Laser Powder Bed Fusion (PBF-LB) technique. Developing models that accurately represent experimental results requires pro ...

SPINN: Advancing Cosmological Simulations of Fuzzy Dark Matter with Physics Informed Neural Networks

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Euclid preparation: LIV. Sensitivity to neutrino parameters

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

Euclid: A complete Einstein ring in NGC 6505

Neural Garment Modeling

Edge-Cloud Collaborated Object Detection via Bandwidth Adaptive Difficult-Case Discriminator

CAPERS-LRD-z9: A Gas-enshrouded Little Red Dot Hosting a Broad-line Active Galactic Nucleus at z = 9.288

The topology of a chaotic attractor in the Kuramoto-Sivashinsky equation

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

Diffusion Stochastic Optimization for Min-Max Problems

t-PiNet: A thermodynamics-informed hierarchical learning for discovering constitutive relations of geomaterials

Heavy neutron stars from light scalars

Search for gamma-ray spectral lines from dark-matter annihilation with the DAMPE satellite

Machine Learning Applications to Conditional Asset Pricing: Attention, CNNs, and Neural Tangent Kernels

Harnessing deep learning to detect bronchiolitis obliterans syndrome from chest CT

Ambipolar diffusion and the mass-to-flux ratio in a turbulent collapsing cloud

On the calibration of thermo-microstructural simulation models for Laser Powder Bed Fusion process: Integrating physics-informed neural networks with cellular automata