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.
Deep learning models enhance engineering design automation.
Automates building structural design with reduced mass and carbon footprint.
Study uses machine learning to optimize seismic design parameters.
A new method optimizes complex engineering designs under uncertainty efficiently.
ANN with GA optimizes flexible disc design for lower mass and stress.
New framework for 3D spatial topology enumeration and identification.
Computer model calibration typically operates by choosing parameter values in a computer model so that the model output faithfully predicts reality. By using performance targets in place of observed data, we show that calibration techniques can be repurposed to wed engineering and material design, two processes that ar…
Improves Bayesian optimisation for engineering design problems with many variables.
This research categorizes AMM designs for secure token exchanges.
MO-PaDGAN generates diverse, high-performance designs with multiple metrics.
New method for mixed-variable GSA improves material design efficiency.
Optimal engine operation during a transient driving cycle is the key to achieving greater fuel economy, engine efficiency, and reduced emissions. In order to achieve continuously optimal engine operation, engine calibration methods use a combination of static correlations obtained from dynamometer tests for steady-stat…
A key phase in the bridge design process is the selection of the structural system. Due to budget and time constraints, engineers typically rely on engineering judgment and prior experience when selecting a structural system, often considering a limited range of design alternatives. The objective of this study was to e…
We provide a methodology, resilient feature engineering, for creating adversarially resilient classifiers. According to existing work, adversarial attacks identify weakly correlated or non-predictive features learned by the classifier during training and design the adversarial noise to utilize these features. Therefore…
Residual generation helps diagnose engine faults using neural networks.
New method refines model predictions as design evolves.
In recent years several trading platforms appeared which provide a backtest engine to calculate historic performance of self designed trading strategies on underlying candle data. The construction of a correct working backtest engine is, however, a subtle task as shown by Maier-Paape and Platen (cf. arXiv:1412.5558 [q-…
ML predicts alloy properties considering chemistry, processing, and data transformations.
Transformer models can approximate smooth functions with prompts, enhancing LLMs' dynamic capabilities.
We consider the problem of identifying the most profitable product design from a finite set of candidates under unknown consumer preference. A standard approach to this problem follows a two-step strategy: First, estimate the preference of the consumer population, represented as a point in part-worth space, using an ad…
Factor Engine simplifies financial factor computation and analysis in Python.
Paper proposes verifier engineering for improving foundation models.
Maximizing the speed and precision of communication while minimizing power dissipation is a fundamental engineering design goal. Also, biological systems achieve remarkable speed, precision and power efficiency using poorly understood physical design principles. Powerful theories like information theory and thermodynam…
Deep learning algorithms excel at extracting patterns from raw data, and with large datasets, they have been very successful in computer vision and natural language applications. However, in other domains, large datasets on which to learn representations from may not exist. In this work, we develop a novel multimodal C…
New method stabilizes tensegrity structures suitable for engineering.
Paper proposes a holistic optimization for civil structures considering uncertainties.
These lecture notes in the De Rham-Hodge theory are designed for a 1-semester undergraduate course (in mathematics, physics, engineering, chemistry or biology). This landmark theory of the 20th Century mathematics gives a rigorous foundation to modern field and gauge theories in physics, engineering and physiology. The…
Deep learning models optimize protein sequences.
MCD automates counterfactual design searches for multi-modal tasks.
MESMOC optimizes constrained multi-objective problems efficiently.
MO-PaDGAN improves multi-objective optimization by generating diverse and high-performing designs.
This paper reviews Gaussian process-based multi-fidelity techniques for different fidelity relationships.
Detecting early signs of failures (anomalies) in complex systems is one of the main goal of preventive maintenance. It allows in particular to avoid actual failures by (re)scheduling maintenance operations in a way that optimizes maintenance costs. Aircraft engine health monitoring is one representative example of a fi…
These lecture notes in Lie Groups are designed for a 1--semester third year or graduate course in mathematics, physics, engineering, chemistry or biology. This landmark theory of the 20th Century mathematics and physics gives a rigorous foundation to modern dynamics, as well as field and gauge theories in physics, engi…
Continuous integration is an indispensable step of modern software engineering practices to systematically manage the life cycles of system development. Developing a machine learning model is no difference - it is an engineering process with a life cycle, including design, implementation, tuning, testing, and deploymen…
Aircraft engine manufacturers collect large amount of engine related data during flights. These data are used to detect anomalies in the engines in order to help companies optimize their maintenance costs. This article introduces and studies a generic methodology that allows one to build automatic early signs of anomal…
REMAL: Residual Equilibrium Manifold Active Learning for Surrogate-Based Multidisciplinary Design Analysis
A new framework uses an Incremental Transformer to design geopolymer mixtures efficiently.
We describe GTApprox - a new tool for medium-scale surrogate modeling in industrial design. Compared to existing software, GTApprox brings several innovations: a few novel approximation algorithms, several advanced methods of automated model selection, novel options in the form of hints. We demonstrate the efficiency o…
Automatic anomaly detection is a major issue in various areas. Beyond mere detection, the identification of the source of the problem that produced the anomaly is also essential. This is particularly the case in aircraft engine health monitoring where detecting early signs of failure (anomalies) and helping the engine …
Blockchain aims to improve trust in AI systems, but lacks systematic studies.
VEST automates feature engineering for time series forecasting.
In this paper, we review recent work published over the last 3 years under the umbrella of Neuromorphic engineering to analyze what are the common features among such systems. We see that there is no clear consensus but each system has one or more of the following features:(1) Analog computing (2) Non vonNeumann Archit…
In this paper, we introduce a physics-driven regularization method for training of deep neural networks (DNNs) for use in engineering design and analysis problems. In particular, we focus on prediction of a physical system, for which in addition to training data, partial or complete information on a set of governing la…
Proposes a framework to fuse heterogeneous data sources for better modeling.
FeatureEnVi aids in feature engineering with visual analytics.
Differentiable pipeline replaces non-differentiable CAE components for shape optimization.
ARCO-BO optimizes multi-agent design under heterogeneity, improving efficiency and performance.