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.
New framework for 3D spatial topology enumeration and identification.
problem Efficient navigation through complex engineering system topologies.
method Mathematical spatial graph theory to represent, enumerate, and identify unique topological classes.
result Identification of distinctive 3D topological classes for engineering systems.
Paper uses deep reinforcement learning for better control of rocket engines during start-up phases.
problem Lack of optimal control during transient phases of liquid rocket engines.
method Deep reinforcement learning approach for optimal control of a gas-generator engine's continuous start-up phase.
result Deep reinforcement learning controller achieves highest performance and minimal computational effort.
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.
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…
Can engineering neural networks be approached in a disciplined way similar to how engineers build software for civil aircraft? We present nn-dependability-kit, an open-source toolbox to support safety engineering of neural networks for autonomous driving systems. The rationale behind nn-dependability-kit is to consider…
Paper integrates ML with physics models for engineering and environmental challenges.
problem Complex science and engineering problems require new methodologies combining physics-based models and ML.
method Structured overview of integrating physics-based models with ML techniques.
result Taxonomy of existing techniques and potential research gaps identified.
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.
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.
Paper derives an error bound for stochastic LTI systems.
problem Stochastic LTI systems with inputs in control engineering and econometrics.
method PAC-Bayesian-Like error bound derivation.
result Derived an error bound for stochastic LTI systems.
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 …
This research categorizes AMM designs for secure token exchanges.
problem Designing AMMs for cryptoeconomic systems can lead to financial risks and inefficiencies.
method Developed an AMM taxonomy and proposed three archetypes.
result AMM archetypes meet key requirements for token issuance and exchange.
ERFit identifies dynamic equations from data with minimal supervision.
problem Data-driven sparse system identification in science and engineering.
method Entropic Regression method.
result ERFit package simplifies sparse system identification for various applications.
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.
CoinTossX is a low-latency, open-source matching engine for financial trading.
problem Efficiently matching orders in financial markets with low latency and high throughput.
method Developed in Java, orders submitted via UDP SBE, low-latency message transport (Aeron Media Driver). Separates order generation and matching.
result Demonstrated low-latency, high-throughput performance in various deployment scenarios.
Confounding biases recommender systems, even when data seems fully observed.
problem Unmeasured features influencing both treatment and outcome in recommender systems.
method Illustrations and simulation studies showing how common practices introduce confounding.
result Standard recommender system practices can introduce confounding, reducing performance.
DeepONets improve surrogate modeling for engineering systems.
problem Accurately modeling complex PDEs for engineering systems.
method DeepONets specialize in approximating mathematical operators for PDEs.
result DeepONets achieve high prediction accuracy and zero-shot capability.
A growing interest has been witnessed recently from both academia and industry in building nearest neighbor search (NNS) solutions on top of full-text search engines. Compared with other NNS systems, such solutions are capable of effectively reducing main memory consumption, coherently supporting multi-model search and…
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…
SAFE automates feature engineering for industrial tasks efficiently and scalably.
problem Efficiency and scalability of automatic feature engineering methods for industrial tasks.
method SAFE (Scalable Automatic Feature Engineering) method, which provides excellent efficiency and scalability.
result SAFE method provides prominent efficiency and competitive effectiveness in industrial tasks.
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…
Paper introduces TS-GPT for engineering time series forecasting.
problem Engineering time series require causal operations, unlike linguistic data.
method Innovations representation theory, Generative Pre-trained Transformer.
result TS-GPT effectively forecasts real-time locational marginal prices.
A new method optimizes complex engineering designs under uncertainty efficiently.
problem Optimizing large, uncertain engineering designs with limited resources.
method Multi-level informed optimization via decomposed Kriging.
result Significantly faster and more accurate optimization compared to state-of-the-art methods.
DeepONet accelerates nuclear DT inference with high accuracy and efficiency.
problem Real-time prediction and model evaluation in nuclear systems.
method Deep Neural Operator (DeepONet) for surrogate modeling.
result DeepONet outperforms traditional ML methods in accuracy and speed.
Meta-learning helps models learn quickly from few samples.
problem Deep learning requires many samples, which are hard to get.
method Meta-learning optimizes models to adapt quickly to new tasks.
result Meta-learning can improve model efficiency and adaptability.
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.
Recommender systems play an essential role in the modern business world. They recommend favorable items like books, movies, and search queries to users based on their past preferences. Applying similar ideas and techniques to Monte Carlo simulations of physical systems boosts their efficiency without sacrificing accura…
STAD improves travel time estimation by learning from real traffic data.
problem Travel time estimation using GPS traces is inaccurate and requires offline optimization.
method STAD uses machine learning and real-time trip data to adjust travel time estimates.
result STAD reduces median absolute errors by 14% in Doha and New York City, and 29% in Porto.
Proposes efficient algorithm for system-level I&M decisions under uncertainty.
problem Optimal management strategies for deteriorating civil engineering systems.
method Factored partially observable Markov decision process with Bayesian networks and DDMAC reinforcement learning.
result DDMAC policies offer substantial benefits over heuristic approaches in system-level cost optimization.
Bayesian optimization simplifies bioprocess engineering experiments.
problem Complex biological systems and experimental uncertainty.
method Adapts classical Bayesian optimization for bioprocess engineering.
result Provides accessible introduction to Bayesian optimization for practitioners.
Mathematical advances needed for Digital Twins, differing from traditional models.
problem Foundational mathematical advances required for Digital Twins.
method Multi-scale, multi-physics modeling and coupling, different reliability criteria and uncertainty assessments.
result AI/ML methods can perform well in biomedical problems but fail in simple engineering systems.
In many instances, information on engineering systems can be obtained through measurements, monitoring or direct observations of system performances and can be used to update the system reliability estimate. In structural reliability analysis, such information is expressed either by inequalities (e.g. for the observati…
Residual generation helps diagnose engine faults using neural networks.
problem Fault diagnosis in engines with unknown classes and limited data.
method Grey-box recurrent neural networks incorporating physical insights.
result Improved fault classification and root cause identification.
Rapid improvements in machine learning over the past decade are beginning to have far-reaching effects. For communications, engineers with limited domain expertise can now use off-the-shelf learning packages to design high-performance systems based on simulations. Prior to the current revolution in machine learning, th…
AutoML enhances credit decisions with XAI for better transparency.
problem Transparency in AI-driven financial decisions.
method Combining AutoML and XAI (SHAP) for credit scoring.
result Improved efficiency and accuracy in credit decisions with enhanced transparency.
Kernel test evaluates dynamical system data streams.
problem Evaluate if data streams from dynamical systems are from the same distribution.
method Proposes a novel kernel two-sample test for dynamical systems, addressing independence and autocorrelation challenges.
result Data-driven method with theoretical guarantees for anomaly detection.
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.
Unified framework for robust causal directionality in quantum systems under MNAR observation.
problem Determining causal directionality in quantum systems under MNAR observation.
method Integrates CVAE-based latent constraints, MNAR-aware selection models, GEE-stabilized regression, penalized empirical likelihood, and Bayesian optimization.
result Achieves lower bias and variance, near-nominal coverage, and superior quantum-specific diagnostics.
A federated learning framework improves RUL prognosis for aircraft engines without sharing data.
problem Limited run-to-failure data samples for accurate RUL prognosis.
method Federated learning framework, decentralized validation, and robust aggregation methods.
result The federated learning framework leads to more accurate RUL prognosis for five out of six airlines.
Hybrid engine analyzes news sentiment for markets in real-time.
problem Real-time market analysis of news sentiment.
method Three-way ensemble learning combining financial lexicon, adaptive TF-IDF clustering, and auto-calibrated weighting.
result Adaptive statistical clustering learner improves adaptability to market changes.
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…
This paper introduces Probability Engineering to improve deep learning models.
problem Challenges in traditional probabilistic modeling for AI applications.
method Treats learned probability distributions as engineering artifacts and actively modifies them.
result Improves robustness, efficiency, adaptability, and trustworthiness of deep learning models.
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.
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.
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…
A theoretical framework that supports automated construction of dynamic prime models purely from experimental time series data has been invented and developed, which can automatically generate (construct) data-driven models of any time series data in seconds. This has resulted in the formulation and formalisation of ne…
Fatal accidents are a major issue hindering the wide acceptance of safety-critical systems using machine-learning and deep-learning models, such as automated-driving vehicles. Quality assurance frameworks are required for such machine learning systems, but there are no widely accepted and established quality-assurance …
Transformer models can approximate smooth functions with prompts, enhancing LLMs' dynamic capabilities.
problem Lack of theoretical framework for prompt engineering in transformer models.
method Formal framework demonstrating transformer models can approximate β-times differentiable functions with prompts. result Transformer models can approximate β-times differentiable functions with arbitrary precision using appropriately structured prompts.