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.
This review discusses challenges and solutions for AI in chemical engineering.
Paper discusses challenges in deploying ML models for structural engineering.
Mean Field Games applied to finance and economics.
This work represents an application of constant mean curvature graphs (as solutions of the mean curvature PDE) to non-linear non-Darcy flows in porous media. It relates time invariant pressure distribution graphs to graphs of constant mean curvature surfaces. This differential geometric interpretation provides an impor…
This paper introduces Probability Engineering to improve deep learning models.
Multi-scanner Antivirus systems provide insightful information on the nature of a suspect application; however there is often a lack of consensus and consistency between different Anti-Virus engines. In this article, we analyze more than 250 thousand malware signatures generated by 61 different Anti-Virus engines after…
We introduce a general procedure called `reverse engineering' that can be used to construct infinite families of smooth 4-manifolds in a given homeomorphism type. As one of the applications of this technique, we produce an infinite family of pairwise nondiffeomorphic 4-manifolds homeomorphic to CP^2#3(-CP^2).
New method stabilizes tensegrity structures suitable for engineering.
This paper presents an engine able to predict jointly the real-time concentration of the main pollutants harming people's health: nitrogen dioxyde (NO2), ozone (O3) and particulate matter (PM2.5 and PM10, which are respectively the particles whose size are below 2.5 um and 10 um). The engine covers a large part of the …
Deep learning models enhance engineering design automation.
Paper introduces TS-GPT for engineering time series forecasting.
New framework for 3D spatial topology enumeration and identification.
Review of MLMC in financial engineering, focusing on option pricing and risk management.
Paper introduces MTGP for engineering tasks with sparse data.
Paper integrates ML with physics models for engineering and environmental challenges.
Bayesian optimization simplifies bioprocess engineering experiments.
Deep learning models optimize protein sequences.
Survey of financial foundation models for diverse applications.
Simple feature engineering beats complex models in financial prediction.
Meta-learning helps models learn quickly from few samples.
The ubiquitous availability of wearable sensors is responsible for driving the Internet-of-Things but is also making an impact on sport sciences and precision medicine. While human activity recognition from smartphone data or other types of inertial measurement units (IMU) has evolved to one of the most prominent daily…
Survey and new results link hydrodynamics, molecular physics, and financial engineering.
FeatureEnVi aids in feature engineering with visual analytics.
Machine learning recently has been used to identify the governing equations for dynamics in physical systems. The promising results from applications on systems such as fluid dynamics and chemical kinetics inspire further investigation of these methods on complex engineered systems. Dynamics of these systems play a cru…
Machine learning algorithms are increasingly influencing our decisions and interacting with us in all parts of our daily lives. Therefore, just like for power plants, highways, and myriad other engineered sociotechnical systems, we must consider the safety of systems involving machine learning. In this paper, we first …
There has been considerable growth and interest in industrial applications of machine learning (ML) in recent years. ML engineers, as a consequence, are in high demand across the industry, yet improving the efficiency of ML engineers remains a fundamental challenge. Automated machine learning (AutoML) has emerged as a …
VEST automates feature engineering for time series forecasting.
Study defines and optimizes bank reliability using LR and PSO.
Examines performance metrics for ML models in engineering.
DeepONet accelerates nuclear DT inference with high accuracy and efficiency.
Paper proposes a new model for better engine control.
Complex black-box predictive models may have high accuracy, but opacity causes problems like lack of trust, lack of stability, sensitivity to concept drift. On the other hand, interpretable models require more work related to feature engineering, which is very time consuming. Can we train interpretable and accurate mod…
Partial Differential Equations (PDE) are fundamental to model different phenomena in science and engineering mathematically. Solving them is a crucial step towards a precise knowledge of the behaviour of natural and engineered systems. In general, in order to solve PDEs that represent real systems to an acceptable degr…
This paper emphasizes the need for uncertainty quantification in data-driven ML models for nuclear engineering.
Paper uses neural nets for financial optimization problems.
There has been a lot of recent interest in adopting machine learning methods for scientific and engineering applications. This has in large part been inspired by recent successes and advances in the domains of Natural Language Processing (NLP) and Image Classification (IC). However, scientific and engineering problems …
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…
Synthesizes machine learning applications in reliability and safety.
Recent advances in learning Deep Neural Network (DNN) architectures have received a great deal of attention due to their ability to outperform state-of-the-art classifiers across a wide range of applications, with little or no feature engineering. In this paper, we broadly study the applicability of deep learning to we…
CBMs improve interpretability in RUL prediction for aircraft engines.
Optimizing an interactive system against a predefined online metric is particularly challenging, when the metric is computed from user feedback such as clicks and payments. The key challenge is the counterfactual nature: in the case of Web search, any change to a component of the search engine may result in a different…
Safe active learning for multi-output Gaussian processes reduces data acquisition costs and ensures safety.
We introduce the Pricing Engine package to enable the use of Double ML estimation techniques in general panel data settings. Customization allows the user to specify first-stage models, first-stage featurization, second stage treatment selection and second stage causal-modeling. We also introduce a DynamicDML class tha…
The use of synthetic data generated by Generative Adversarial Networks (GANs) has become quite a popular method to do data augmentation for many applications. While practitioners celebrate this as an economical way to get more synthetic data that can be used to train downstream classifiers, it is not clear that they re…
AI agent improves performance attribution analysis with high accuracy.
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…
Surveying risk measures for handling uncertainty in various fields.
The goal of this article is to describe the concepts of system dynamics and its applications to the simulation modeling of financial institutions daily activity. The hybrid method of the re-engineering of banking business processes based upon combination of system dynamics, queuing theory and tools of ordinary differen…