We study a multiplicative transient price impact model for an illiquid financial market, where trading causes price impact which is multiplicative in relation to the current price, transient over time with finite rate of resilience, and non-linear in the order size. We construct explicit solutions for the optimal contr…
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48 results for “transient control”
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.
We study optimal liquidation in the presence of linear temporary and transient price impact along with taking into account a general price predicting finite-variation signal. We formulate this problem as minimization of a cost-risk functional over a class of absolutely continuous and signal-adaptive strategies. The sto…
First-passage times in random walks have a vast number of diverse applications in physics, chemistry, biology, and finance. In general, environmental conditions for a stochastic process are not constant on the time scale of the average first-passage time, or control might be applied to reduce noise. We investigate mome…
New method uses machine learning to analyze catalyst reactions.
problem Understanding complex reaction mechanisms in catalytic materials.
method Combining transient kinetics and machine learning.
result Correct estimates of micro-kinetic coefficients and mechanism.
Study uses deep reinforcement learning for real-time control of nuclear microreactors, achieving similar or superior performance to traditional PID controllers.
problem Minimizing operating costs of nuclear microreactors through autonomous control, especially in load-following scenarios.
method Application of deep reinforcement learning (RL) for real-time drum control in microreactors, using point kinetics model with thermal and xenon feedback.
result Deep reinforcement learning controllers, including single- and multi-agent RL frameworks, can achieve similar or superior load-following performance to traditional PID control across various scenarios.
Predicts the age of astronomical transients from real-time data.
problem Improving understanding of transients and their progenitor systems.
method Bayesian probabilistic recurrent neural network.
result Accurately predicts the age of transients with robust uncertainties.
We give a complete solution to the problem of minimizing the expected liquidity costs in presence of a general drift when the underlying market impact model has linear transient price impact with exponential resilience. It turns out that this problem is well-posed only if the drift is absolutely continuous. Optimal str…
New Roman pipeline detects astronomical transients.
problem Automated detection of transients from Roman Space Telescope data.
method Machine learning model RuBR for distinguishing real from fake detections.
result Effective real-bogus classification in Roman era.
Trans-Bifurcation Prediction of Dynamics in terms of Extreme Learning Machines with Control Inputsnlin.CD
Extremely accurate prediction of dynamical system bifurcations using control inputs.
problem Predicting complex bifurcation structures in dynamical systems.
method Extending extreme learning machines with control inputs to model system dynamics.
result The model can nearly reproduce the entire structure of bifurcations using only a few parameter values.
Calculates the transient number of knots using homology groups.
problem Determining the minimal number of arcs needed to trivialize a knot.
method Uses the rank of the first homology group of the double branched cover of the knot.
result Calculates transient numbers for many knots and shows existence of knots with arbitrarily large transient numbers.
Detects anomalies in astronomical time series data.
problem Identifying new and interesting transients in large astronomical surveys.
method Two novel methods: a probabilistic neural network and a Bayesian parametric model.
result Neural networks are less suitable for anomaly detection in time series data compared to parametric models.
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…
Transient market impact explained via Nash equilibrium in a game.
problem Understanding the transient nature of market impact.
method Analyzing a game between a trader and an arbitrageur, deriving decay kernels.
result Implied transient impact can be derived from trader behavior at Nash equilibrium.
The calibration of a reservoir model with observed transient data of fluid pressures and rates is a key task in obtaining a predictive model of the flow and transport behaviour of the earth's subsurface. The model calibration task, commonly referred to as "history matching", can be formalised as an ill-posed inverse pr…
Study non-rectangular robust MDPs for average-reward, finding optimal policies and transient values.
problem Non-rectangular robust Markov decision processes under average-reward criterion.
method Proves history-dependent policies are robust-optimal, introduces transient-value framework, constructs epoch-based policy.
result Existence and properties of robust optimal policies, transient value bounds.
New method decomposes Markov chain rewards into persistent and transient components.
problem Ambiguity in classical evaluation methods for Markov chains with reducible and periodic states.
method Minimal exact quotient by the real peripheral invariant subspace, decomposing rewards into persistent and transient components.
result Exact comparison with classical methods shows that the new decomposition reallocates the same information, making persistent modes explicit.
Study examines the training process of an unsupervised learning model for detecting gravitational-wave transient noise.
problem Transient noise in gravitational-wave detector data causes instability and signal overlap.
method Unsupervised deep learning with variational autoencoder and invariant information clustering applied to the Gravity Spy dataset.
result Training process of the unsupervised learning architecture is examined and reported.
We present RAPID (Real-time Automated Photometric IDentification), a novel time-series classification tool capable of automatically identifying transients from within a day of the initial alert, to the full lifetime of a light curve. Using a deep recurrent neural network with Gated Recurrent Units (GRUs), we present th…
Study solves utility maximization in a transient price impact market.
problem Utility maximization in a market with transient price impact.
method Developed a discrete-time model and removed market depth and resilience process restrictions.
result Solved the utility maximization problem without convexity of attainable portfolio values.
We give a singular control approach to the problem of minimizing an energy functional for measures with given total mass on a compact real interval, when energy is defined in terms of a completely monotone kernel. This problem occurs both in potential theory and when looking for optimal financial order execution strate…
New method corrects motion artifacts in MR images without paired data.
problem Lack of paired data for supervised training in deep learning for MR motion correction.
method Outlier-rejecting bootstrap subsampling and aggregation, using optimal transport cycleGAN.
result Outperforms existing deep learning methods in correcting motion artifacts from TSM.
Novel method uses information theory to measure causal influences during transient neural events.
problem Characterizing network interactions during transient neural events.
method Structural Causal Models, Information Theory, Transfer Entropy, Dynamic Causal Strength, Relative Dynamic Causal Strength.
result Introduced a novel measure, relative Dynamic Causal Strength, with theoretical and empirical support.
Resilient Learning-Based Control for Synchronization of Passive Multi-Agent Systems under Attackeess.SY
In this paper, we show synchronization for a group of output passive agents that communicate with each other according to an underlying communication graph to achieve a common goal. We propose a distributed event-triggered control framework that will guarantee synchronization and considerably decrease the required comm…
Study uses machine learning to optimize stock trading strategies.
problem Optimizing stock trading strategies with machine learning.
method Dynamic programming and deep learning for nonlinear price impact.
result NN surrogates accurately approximate optimal strategies.
Unsupervised learning classifies transient noise in gravitational wave detectors.
problem Transient noise interferes with gravitational wave signals, causing instability.
method Combines variational autoencoder and invariant information clustering.
result Consistent classification with Gravity Spy project labels.
We discuss an "extrinsic" property of knots in a 3-subspace of the 3-sphere to characterize how the subspace is embedded in . Specifically, we show that every knot in a subspace of the 3-sphere is transient if and only if the exterior of the subspace is a disjoint union of handlebodies, i.e. regular neighbor…
CNNs improve transient detection in DES-SN images.
problem Automated identification of transients in astronomical images.
method Convolutional Neural Networks (CNNs) for image classification.
result CNNs achieve efficiency in identifying non-artifacts from artifacts.
We solve explicitly a two-dimensional singular control problem of finite fuel type for infinite time horizon. The problem stems from the optimal liquidation of an asset position in a financial market with multiplicative and transient price impact. Liquidity is stochastic in that the volume effect process, which determi…
A Mean-Field Control Problem of Optimal Portfolio Liquidation with Semimartingale Strategiesq-fin.MF
Study optimizes portfolio liquidation strategies with complex market impacts.
problem Optimizing portfolio liquidation with transient market impacts and self-exciting order flow.
method Mean-field control problem with semimartingale strategies, passing to continuous-time limit, and solving Riccati equations.
result Existence of optimal strategy with jumps only at start and end of trading period.
Paper presents a deep learning framework for faster, more accurate nuclear reactor power prediction.
problem Inaccurate and inefficient modeling of nuclear reactor transients.
method Hybrid digital twin-focused multi-stage deep learning framework using feed-forward neural networks.
result Achieved remarkable accuracy (96% classification, 2.3% MAPE) with noise-enhanced simulated data.
Study optimal execution in a transient price impact model with multiple traders.
problem Optimal execution among multiple traders with transient price impact.
method Analyzed -player optimal execution games in an Obizhaeva--Wang model with and without regularization. Derived equilibrium solutions and explained their behavior.
result Existence of equilibrium restored with a specific time-dependent cost on block trades, and equilibrium is tractable.
New algorithm improves understanding of decentralized SBO transient iteration complexity.
problem Limited understanding of how network topology, data heterogeneity, and nested structures affect SBO.
method D-SOBA framework with two variants: D-SOBA-SO and D-SOBA-FO, providing non-asymptotic convergence analysis and transient iteration complexity.
result First theoretical understanding of how network topology, data heterogeneity, and nested structures influence decentralized SBO.
SMC analysis reveals key transient effects in macroeconomic ABM.
problem Analysis of complex ABMs is challenging and often relies on ad hoc methods.
method Statistical model checking (SMC) implemented through MultiVeStA.
result Clear contrast across parameter families in macro-financial and structural sweeps.
Escaping the Brownian stalkersq-fin.TR
We propose a simple model for the behaviour of longterm investors on a stock market, consisting of three particles, which represent the current price of the stock and the opinion of the buyers, respectively sellers, about the right trading price. As time evolves, both groups of traders update their opinions with respec…
Develops a framework for valuing Asian options with market impact.
problem Valuation of Asian options under price impact.
method Discrete-time quote-level model, continuous-time limits, Hamilton-Jacobi-Bellman equations, CRR-style tree-based Bellman algorithm.
result Endogenous trading volumes feed into prices and costs, leading to nontrivial bid-ask spreads.
Random matrix theory explains transient signal detectability in early-stopped gradient flow.
problem Transient signal detectability in early-stopped gradient flow.
method Random matrix theory applied to gradient flow in a linear teacher-student setting.
result Transient Baik-Ben Arous-Péché (BBP) transition in learning dynamics due to anisotropy and noise.
We present a sparse and invariant representation with low asymptotic complexity for robust unsupervised transient and onset zone detection in noisy environments. This unsupervised approach is based on wavelet transforms and leverages the scattering network from Mallat et al. by deriving frequency invariance. This frequ…
Improves inverse uncertainty quantification for time-dependent data using PCA and deep neural networks.
problem Efficiently quantify model input uncertainties from time-dependent experimental data.
method Functional PCA for dimensionality reduction, deep neural networks for surrogate modeling, Bayesian neural networks for uncertainty estimation.
result The proposed method reduces the computational cost and improves the agreement with experimental data.
A new kernel improves statistical surrogates for stochastic manifolds with diverse data.
problem Handling statistical surrogates for stochastic manifolds with heterogeneous data.
method A transient anisotropic kernel is introduced to improve statistical surrogates for stochastic manifolds with heterogeneous data.
result The transient anisotropic kernel provides a better representation of statistical dependencies in the learned probability measure.
Bayesian BIC for multi-trial data improves VAR model order selection.
problem Optimal VAR model order selection for multi-trial event-based data.
method Derive and apply Bayesian Information Criterion (BIC) for multi-trial ensemble data.
result Multi-trial BIC successfully recovers real model order and estimates small model order.
A new ML method speeds up PDE simulations without needing classical training.
problem Accelerating transient PDE simulations using machine learning.
method Online-learned preconditioners using a bandit algorithm.
result One-shot acceleration of PDE simulations.
This study examines how market makers balance risk and impact in foreign exchange markets.
problem Balancing risk management with market impact in foreign exchange markets.
method An intermediate scenario approach considering both instantaneous and permanent market impact components.
result Transient market impact is more prevalent than previously thought, challenging traditional market impact models.
TKRR improves KRR performance by aligning target functions with kernels.
problem Improving kernel ridge regression performance through target alignment.
method Focuses on truncated kernel ridge regression (TKRR) with an additional spectral truncation parameter.
result TKRR can achieve faster rates than full KRR, reaching parametric rates.
We solve the superhedging problem for European options in an illiquid extension of the Black-Scholes model, in which transactions have transient price impact and the costs and the strategies for hedging are affected by physical or cash settlement requirements at maturity. Our analysis is based on a convenient choice of…
Optimal liquidation strategy with price impact and signal exploitation.
problem Maximizing revenue-risk in a market with transient and temporary price impact.
method Infinite dimensional stochastic control approach, backward stochastic differential equation, operator-valued Riccati equation.
result Explicit expression for the optimal trading strategy.
We study the problem of the optimal execution of a large trade in the presence of nonlinear transient impact. We propose an approach based on homotopy analysis, whereby a well behaved initial strategy is continuously deformed to lower the expected execution cost. We find that the optimal solution is front loaded for co…
Optimal energy trading strategy for intraday markets using Hawkes processes.
problem Optimal execution in intraday energy markets with specific trading patterns.
method Calibrated Hawkes process model with transient price impact.
result Substantial cost reductions in TWAP and VWAP benchmarks.