A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Bézier-GAN optimizes airfoil design by reducing shape complexity.
New shape representation for airfoils improves design and manufacturing.
New approach reduces shape optimization anomalies and improves design quality.
Optimizes hydrokinetic turbine design using morphing and Bayesian optimization.
Generative thermal design learns optimal shapes using multi-agent reinforcement learning.
Design-by-Morphing creates radical airfoil designs without geometric constraints.
Many real-world objects are designed by smooth curves, especially in the domain of aerospace and ship, where aerodynamic shapes (e.g., airfoils) and hydrodynamic shapes (e.g., hulls) are designed. To facilitate the design process of those objects, we propose a deep learning based generative model that can synthesize sm…
Differentiable pipeline replaces non-differentiable CAE components for shape optimization.
METASET selects diverse unit cells for efficient data-driven metamaterial design.
Optimizing fluid-dynamic performance is an important engineering task. Traditionally, experts design shapes based on empirical estimations and verify them through expensive experiments. This costly process, both in terms of time and space, may only explore a limited number of shapes and lead to sub-optimal designs. In …
We propose a data-driven 3D shape design method that can learn a generative model from a corpus of existing designs, and use this model to produce a wide range of new designs. The approach learns an encoding of the samples in the training corpus using an unsupervised variational autoencoder-decoder architecture, withou…
We introduce and develop fine shape, which has a very simple definition and aims to supersede all previously known shape theories for metrizable spaces. The problem with known shape theories of metrizable spaces is illustrated by the following bizarre situation. Čech cohomology is an invariant of shape, and a fortiori …
A new geometric shaping method is proposed, leveraging unsupervised machine learning to optimize the constellation design. The learned constellation mitigates nonlinear effects with gains up to 0.13 bit/4D when trained with a simplified fiber channel model.
Enhanced aerodynamic design using machine learning and Gaussian processes.
Python tools for 3D shape analysis on Kendall's space.
New method allows sheets to morph into multiple shapes via spatially varying stimuli.
Paper develops formulas for shape derivatives in wave scattering.
ML predicts alloy properties considering chemistry, processing, and data transformations.
Paper introduces a method to generate stable shapes using Grassmann manifolds.
Reward shaping is one of the most effective methods to tackle the crucial yet challenging problem of credit assignment in Reinforcement Learning (RL). However, designing shaping functions usually requires much expert knowledge and hand-engineering, and the difficulties are further exacerbated given multiple similar tas…
Microfluidic devices are utilized to control and direct flow behavior in a wide variety of applications, particularly in medical diagnostics. A particularly popular form of microfluidics -- called inertial microfluidic flow sculpting -- involves placing a sequence of pillars to controllably deform an initial flow field…
Generates tubular and membranous shapes using curvature functionals.
Shape adaptor learns flexible resizing factors for neural networks.
Recent reinforcement learning (RL) approaches have shown strong performance in complex domains such as Atari games, but are often highly sample inefficient. A common approach to reduce interaction time with the environment is to use reward shaping, which involves carefully designing reward functions that provide the ag…
We design non-singular cloaks enabling objects to scatter waves like objects with smaller size and very different shapes. We consider the Schrodinger equation which is valid e.g. in the contexts of geometrical and quantum optics. More precisely, we introduce a generalized non-singular transformation for star domains, a…
The choice of constellations largely affects the performance of communication systems. When designing constellations, both the locations and probability of occurrence of the points can be optimized. These approaches are referred to as geometric and probabilistic shaping, respectively. Usually, the geometry of the const…
New method for partial matching of shapes with Varifolds.
The paper uses 3D shapes to reveal sundial design adjustments based on latitude.
Introduces Star-Shaped DDPMs for non-Gaussian distributions.
The paper presents a method for analyzing shape graphs using specific features.
Shapes can roll downhill following any curve, but often return to initial orientation after crossing multiple copies.
Analog BNNs perform similarly regardless of noise distribution shape.
Novel method for shape optimization of non-smooth PDEs.
UniShape improves time series classification by selecting relevant subsequences.
New Hopf algebras help classify 4D shapes.
We consider market players with tail-risk-seeking behaviour as exemplified by the S-shaped utility introduced by Kahneman and Tversky. We argue that risk measures such as value at risk (VaR) and expected shortfall (ES) are ineffective in constraining such players. We show that, in many standard market models, product d…
Clustering partitions a dataset such that observations placed together in a group are similar but different from those in other groups. Hierarchical and -means clustering are two approaches but have different strengths and weaknesses. For instance, hierarchical clustering identifies groups in a tree-like structure b…
A language for specifying complex reinforcement learning tasks.
TLRS improves predictive power of mined formulaic alpha factors.
Topology design optimization offers tremendous opportunity in design and manufacturing freedoms by designing and producing a part from the ground-up without a meaningful initial design as required by conventional shape design optimization approaches. Ideally, with adequate problem statements, to formulate and solve the…
Piecewise Linear-Quadratic (PLQ) penalties are widely used to develop models in statistical inference, signal processing, and machine learning. Common examples of PLQ penalties include least squares, Huber, Vapnik, 1-norm, and their asymmetric generalizations. Properties of these estimators depend on the choice of pena…
Highly expressive models such as deep neural networks (DNNs) have been widely applied to various applications. However, recent studies show that DNNs are vulnerable to adversarial examples, which are carefully crafted inputs aiming to mislead the predictions. Currently, the majority of these studies have focused on per…
We consider the results of combining two approaches developed for the design of Riemannian metrics on curves and surfaces, namely parametrization-invariant metrics of the Sobolev type on spaces of immersions, and metrics derived through Riemannian submersions from right-invariant Sobolev metrics on groups of diffeomorp…
Paper tackles multivariate shape-constrained convex regression problems.
Presents STRIPE model for probabilistic forecasting of non-stationary time series.
A new coordinate system for SPD matrices simplifies computations and generative modeling.
Designing of touchless user interface is gaining popularity in various contexts. Using such interfaces, users can interact with electronic devices even when the hands are dirty or non-conductive. Also, user with partial physical disability can interact with electronic devices using such systems. Research in this direct…
New RL approach infers optimal policies via variational inference.