Feature engineering is a crucial step in the process of predictive modeling. It involves the transformation of given feature space, typically using mathematical functions, with the objective of reducing the modeling error for a given target. However, there is no well-defined basis for performing effective feature engin…
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48 results for “process engineering”
Researchers simulate and estimate a market model with a matching engine to understand its impact on order submission and management.
problem The impact of a matching engine on the modeling of order submission and management in financial markets.
method Simulation of a 10-variate Hawkes process with rules for different order types, including limit orders, to compare model parameters with the original order generating process.
result Practical considerations, not directly related to model specification, can significantly distort the true model specification in an asynchronous trading environment.
New method stabilizes tensegrity structures suitable for engineering.
problem Tensegrity structures often have unstable modes unsuitable for engineering.
method Proposes a relationship between rods and strings for full-rank convexity.
result Designs a stable three-rod three-string tensegrity.
Paper introduces MTGP for engineering tasks with sparse data.
problem Challenges of data sparsity and varying task correlations in engineering.
method Multi-Task Gaussian Processes (MTGP) framework.
result Improves predictive performance and reduces computational costs.
Paper proposes hybrid machine learning for tuning first principles models in engineering systems.
problem Inaccurate first principles models in process engineering due to changing conditions.
method Hybrid machine learning framework using Bayesian Neural Networks.
result Uncertainty estimates improve operation decisions in multiphase flow modeling.
Safe active learning for multi-output Gaussian processes reduces data acquisition costs and ensures safety.
problem Expensive data acquisition and safety concerns in multi-output regression problems.
method Proposes a safe active learning approach considering data informativeness and safety constraints.
result Improved convergence compared to competitors on simulated and real-world datasets.
Feature engineering workflow for activity recognition from synchronized inertial measurement unitscs.LG
Automates feature extraction for IMU-based activity recognition.
problem Manual feature engineering is time-consuming and limits IMU-based applications.
method FRESH algorithm for automated feature extraction.
result Workflow automates feature extraction from synchronized IMU sensors.
Improves Bayesian optimisation for engineering design problems with many variables.
problem Efficiently searching for global minima in high-dimensional design spaces.
method Integrates input and output data to identify a reduced latent subspace using probabilistic partial least squares.
result Significant improvements in convergence to the global minimum compared to existing methods.
Paper reviews neuromorphic engineering features and compares analog vs digital systems.
problem Lack of consensus and unclear features in neuromorphic engineering.
method Review of recent work, comparison of machine learning accelerator chips.
result Analog processing and reduced bit precision architectures offer best efficiencies.
Study shows topological features improve time series classification.
problem Classifying stochastic processes with varying noise and sampling.
method Topological data analysis features compared to statistical and raw features.
result Topological features lead to better classification performance.
Paper proposes verifier engineering for improving foundation models.
problem Challenges in providing effective supervision signals for foundation models.
method Leverages automated verifiers to perform verification tasks and deliver feedback.
result Verifier engineering can enhance foundation models' capabilities.
FeatureEnVi aids in feature engineering with visual analytics.
problem Insufficient support for feature engineering in visual analytics tools.
method Stepwise selection and semi-automatic extraction approaches.
result Extracts heavily engineered features evaluated by multiple metrics.
Physics-informed machine learning for composition-process-property alloy design: shape memory alloy demonstrationcond-mat.mtrl-sci
ML predicts alloy properties considering chemistry, processing, and data transformations.
problem Designing and predicting alloy properties in high-dimensional design space.
method Physics-informed machine learning with engineered features from chemistry and heat treatment.
result ML models accurately predict alloy properties, including hysteresis in shape memory alloys.
Predicts coherence from quantum heat engine noise using machine learning.
problem Predicting coherence in quantum heat engines from nonequilibrium fluctuations.
method Developed a machine learning protocol using K-Nearest Neighbor (KNN) model.
result Machine learning successfully predicts coherence from quantum heat engine noise.
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…
Automates building structural design with reduced mass and carbon footprint.
problem Time-consuming and laborious manual design process for buildings.
method Formulated building structures as graphs, trained end-to-end pipeline with a differentiable simulator.
result Optimal structural designs comparable to GA, with reduced building mass and carbon footprint.
Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparisoneess.SY
Review and compare model order reduction methods for process engineering.
problem Creating computationally efficient yet accurate models for real-time applications.
method Nonlinear model order reduction methods, including general-purpose and tailored approaches for chemical processes.
result Comparison of eight model order reduction methods applied to an air separation process model.
System helps engineers with concept recognition for SEVA.
problem Recognizing systems engineering concepts for SEVA.
method Token classification task, domain expert labeling, pre-trained model fine-tuning, essential datasets creation, knowledge graph construction.
result System successfully recognizes systems engineering concepts.
This paper reviews Gaussian process-based multi-fidelity techniques for different fidelity relationships.
problem Combining accurate and cheap models for complex system design.
method Gaussian process-based multi-fidelity modeling techniques for varying fidelity relationships.
result Comparison of techniques on analytical and aerospace engineering problems.
Deep learning models optimize protein sequences.
problem Optimizing protein properties through sequence design.
method Deep generative models guided by machine learning.
result Improved protein sequence generation from prior knowledge.
The Limits of Complexity: Why Feature Engineering Beats Deep Learning in Investor Flow Predictionq-fin.CP
Simple feature engineering beats complex models in financial prediction.
problem Understanding when complex models outperform simple alternatives in financial prediction.
method Independent Component Analysis (ICA), Wavelet Coherence, Long Short-Term Memory (LSTM) networks with attention mechanisms.
result A simple linear model using normalized flows achieves superior returns compared to complex models.
Automation engineering is the task of integrating, via software, various sensors, actuators, and controls for automating a real-world process. Today, automation engineering is supported by a suite of software tools including integrated development environments (IDE), hardware configurators, compilers, and runtimes. The…
Proposes a model for predicting events from event streams.
problem Predicting events like part replacement and failure in manufacturing and teleservice systems.
method Non-parametric prognostic framework using MGCP modulated Poisson processes.
result MGCP prior facilitates sharing of information and analysis of flexible event patterns.
This paper presents efficient sampling methods for Gaussian processes.
problem High cost of global sensitivity analysis and optimization due to limited high-quality observations.
method Two sampling methods: random Fourier features and pathwise conditioning.
result Efficient generation of posterior samples from Gaussian processes at reduced computational cost.
A Conditional Generative Model for Predicting Material Microstructures from Processing Methodseess.IV
Microstructures of a material form the bridge linking processing conditions - which can be controlled, to the material property - which is the primary interest in engineering applications. Thus a critical task in material design is establishing the processing-structure relationship, which requires domain expertise and …
Oja's rule improves neural network training without engineered tricks.
problem Training deep neural networks with biological constraints.
method Incorporating Oja's plasticity rule into error-driven training.
result Stable, efficient learning in feedforward and recurrent architectures.
Unified strategy for efficient data compression and model estimation.
problem Limited interactive exploration and data interaction in linear model development and deployment.
method Conditionally sufficient statistics for optimal data compression and estimation of linear models.
result Linear models can be estimated from compressed data without loss of parameters or covariances.
An innovative method optimizes engine calibration to improve efficiency and reduce emissions.
problem Complex engines with many tunable parameters require efficient calibration methods.
method Combines Principal Component Decomposition with constrained Bayesian Optimization to minimize pressure curve deviation.
result Optimal engine calibration found after 64.4s with a 0.017% efficiency gain.
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 …
Survey and new results link hydrodynamics, molecular physics, and financial engineering.
problem Understanding financial engineering topics like Asian options and volatility swaps.
method Linking Kevin waves, Klein-Kramers, and Kolmogorov equations to financial models.
result Corrected the original solution of the Kolmogorov equation.
This paper describes the autofeat Python library, which provides scikit-learn style linear regression and classification models with automated feature engineering and selection capabilities. Complex non-linear machine learning models, such as neural networks, are in practice often difficult to train and even harder to …
BERT models outperform GPT in financial engineering sentiment analysis.
problem Using sentiment from news events for commodity trading.
method Benchmarked Transformer models (BERT, GPT) on financial engineering task.
result CopBERT models outperform GPT and vanilla BERT models.
AI agent improves performance attribution analysis with high accuracy.
problem Improving accuracy in performance attribution analysis.
method Leveraging large language models and advanced prompt engineering techniques.
result Achieves accuracy rates exceeding 93% in analyzing performance drivers.
The principles of adaptation in organisms and machines II: Thermodynamics of the Bayesian brainq-bio.NC
The article explores how organisms and machines learn and recognize the world using Bayesian inference and thermodynamics.
problem Understanding how organisms and machines learn and recognize the world.
method Introducing a thermodynamic view of the Bayesian brain hypothesis, using a simple generative model of spiking neural populations.
result The process of Bayesian inference can be quantified using entropy, revealing the perceptual capacity of neural activity.
VEST automates feature engineering for time series forecasting.
problem Challenges in time series forecasting with improved performance.
method VEST combines auto-regression with statistical summarization of recent past dynamics.
result VEST significantly improves forecasting performance.
Surrogate models improve chemical process equipment design and optimization.
problem Design and optimization of complex chemical processes.
method Development and validation of CFD simulators, active learning strategies, and regression techniques.
result Gaussian process regression outperformed other techniques in benchmarking.
Paper uses PCE to quantify ML model and input uncertainties.
problem Accurately quantify and propagate combined uncertainties in ML predictions.
method Polynomial Chaos Expansion (PCE) for joint input and model uncertainty.
result Efficient and accurate calculation of output variability and sensitivity.
Deep learning models enhance engineering design automation.
problem Design optimization and customization across various industries.
method Leveraging deep neural networks, GANs, VAEs, and DRL for design synthesis.
result Recent advances in DGMs show promise in structural optimization, materials design, and shape synthesis.
This essay examines how what is considered to be artificial intelligence (AI) has changed over time and come to intersect with the expertise of the author. Initially, AI developed on a separate trajectory, both topically and institutionally, from pattern recognition, neural information processing, decision and control …
GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end. The distinguishing features of GPflow are that it uses variational inference as the primary approximation method, provides concise code through the use of automatic differentiation, has been engineered with…
This monograph presents a class of algorithms called coordinate descent algorithms for mathematicians, statisticians, and engineers outside the field of optimization. This particular class of algorithms has recently gained popularity due to their effectiveness in solving large-scale optimization problems in machine lea…
Feature engineering has been the key to the success of many prediction models. However, the process is non-trivial and often requires manual feature engineering or exhaustive searching. DNNs are able to automatically learn feature interactions; however, they generate all the interactions implicitly, and are not necessa…
Engineers widely use Gaussian process regression framework to construct surrogate models aimed to replace computationally expensive physical models while exploring design space. Thanks to Gaussian process properties we can use both samples generated by a high fidelity function (an expensive and accurate representation …
FLFE improves machine learning by efficiently and securely transforming features.
problem Efficiently and securely transforming features in a multi-party setting.
method FLFE uses a pre-learning pattern to selectively transform features, reducing communication overhead.
result FLFE outperforms evaluation-based approaches in feature transformation efficiency.
This paper describes HyperStream, a large-scale, flexible and robust software package, written in the Python language, for processing streaming data with workflow creation capabilities. HyperStream overcomes the limitations of other computational engines and provides high-level interfaces to execute complex nesting, fu…
ABM automates feature engineering and variable selection for loss-based models.
problem Improving model performance through better feature engineering and variable selection.
method ABM uses group and fused lasso regularization to automatically select cutting points and variables.
result ABM integrates feature engineering, variable selection, and model training.
Real-time Adaptation for Condition Monitoring Signal Prediction using Label-aware Neural Processescs.LG
Neural process model improves real-time condition monitoring signal prediction.
problem Real-time adaptation for complex condition monitoring signals.
method Label-aware neural processes encoding and reconstruction.
result Advantages in real-time adaptation, enhanced signal prediction with uncertainty quantification, and joint prediction for labels and signals.
Heterogenous Multi-Source Data Fusion Through Input Mapping and Latent Variable Gaussian Processstat.ML
Proposes a framework to fuse heterogeneous data sources for better modeling.
problem Heterogeneous data sources with different input parameter spaces.
method Input mapping calibration (IMC) and latent variable Gaussian process (LVGP).
result Improved predictive accuracy over single source models.