The interpretability of machine learning, particularly for deep neural networks, is crucial for decision making in real-world applications. One approach is replacing the un-interpretable machine learning model with a surrogate model, which has a simple structure for interpretation. Another approach is understanding the…
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48 results for “simulation framework”
Efficient Bayesian decision-making with intractable likelihoods.
problem Bayesian decision-making under intractable likelihoods.
method Learning surrogate models and using simulation-based inference and Bayesian optimization.
result Optimal actions can be learned with fewer simulations than posterior inference.
A general framework for modeling and dynamic simulation of multibody systems using factor graphscs.RO
A new framework models and simulates multibody systems using factor graphs.
problem Solving kinematic and dynamic problems for multi-body systems.
method Factor graph theory for modeling and simulation of multibody systems.
result The proposed framework provides a unified approach for multibody systems.
This paper develops a new framework to assess crypto portfolio risk using simulation methods.
problem Traditional financial risk models fail to capture crypto market characteristics like volatility and contagion.
method The framework integrates four components: volatility stress testing, hedging, contagion modeling, and Monte Carlo simulation.
result The framework robustly assesses crypto portfolio risk and is validated with real data.
Efficient Calibration of Multi-Agent Simulation Models from Output Series with Bayesian Optimizationq-fin.ST
The paper proposes a framework to calibrate multi-agent simulation models from output series using Bayesian optimization.
problem Calibrating multi-agent simulation models from observable output series.
method Novel eligibility set concept, two-sample Kolmogorov-Smirnov test with Bonferroni correction, Bayesian optimization (BO), and trust-region BO (TuRBO).
result Demonstrated the efficiency of the proposed framework using numerical experiments.
Framework creates fast, interpretable surrogates for stochastic simulators with unbounded randomness.
problem Creating accurate and fast approximations for stochastic simulators with unbounded randomness.
method Probabilistic surrogate networks that retain structure of reference simulators and enable amortized inference.
result Surrogates accurately model stochastic programs with unbounded random variables and significantly speed up inference.
Machine learning speeds up GPR simulations.
problem Computational demands of simulating practical GPR problems.
method Automatic ML-based forward solver framework using gprMax.
result Near-real-time GPR simulations achieved.
Proposes ABIDES-Gym for financial markets simulation.
problem Training multi-agent systems in financial markets.
method Wrapping DEMAS into OpenAI Gym framework.
result Developed two financial markets OpenAI Gym environments.
Simulator imperfection, often known as model error, is ubiquitous in practical data assimilation problems. Despite the enormous efforts dedicated to addressing this problem, properly handling simulator imperfection in data assimilation remains to be a challenging task. In this work, we propose an approach to dealing wi…
This paper aims to propose a novel deep learning-integrated framework for deriving reliable simulation input models through incorporating multi-source information. The framework sources and extracts multisource data generated from construction operations, which provides rich information for input modeling. The framewor…
Framework for pricing waterfall structures using simulation and uncertainty modeling.
problem Pricing complex structured finance instruments under uncertainty.
method Simulation-based uncertainty modeling, calibrated probability distributions, PyTorch implementation, Adjoint Algorithmic Differentiation (AAD).
result Efficient gradient computation for risk sensitivity analysis and optimization.
Graph Network-based Simulators learn complex physics simulations.
problem Simulating complex physical systems with high accuracy and scalability.
method Graph Network framework for particle dynamics, learned message-passing.
result Model generalizes well to different conditions and scales, robust to hyperparameters.
New framework tests AVs as a black box, prioritizing rare failure modes.
problem Lack of rigorous and scalable testing methods for AVs.
method Developed a simulation testing framework that learns to identify and rank failure scenarios via adaptive importance-sampling methods.
result First independent evaluation of a full-stack commercial AV system (Comma AI's OpenPilot).
QPyTorch simplifies low-precision training simulations.
problem Empirical evaluation of low-precision training algorithms.
method Native PyTorch framework with efficient fused-kernel approach.
result Supports various low-precision configurations and rounding options.
A framework for simulating systemic risk and its application to the South African banking sectorq-fin.RM
We present a network-based framework for simulating systemic risk that considers shock propagation in banking systems. In particular, the framework allows the modeller to reflect a top-down framework where a shock to one bank in the system affects the solvency and liquidity position of other banks, through systemic mar…
New RL framework simulates financial market dynamics.
problem Complex financial market dynamics under various scenarios.
method Two RL families learn simultaneously, using Deep RL and parametrized reward.
result Agents learn a shared policy for diverse behaviors.
Framework simulates market microstructure with stable Hawkes processes.
problem Reproduce realistic market order flow dynamics.
method Deterministic C++ LOB simulator with Hawkes-driven stochastic order flow.
result Derives stability and ergodicity proofs for Hawkes models.
Program Synthesis for Simulation-Based Inference: Joint Model Selection and Parameter Estimationcs.LG
Framework synthesizes programs for simulating complex models and estimating parameters.
problem Parameter estimation for complex models requires manual encoding of fixed model structures.
method Combines LLMs for program synthesis with neural simulation-based inference.
result Identifies plausible model families from open-ended prompts with high accuracy.
Proposes a new simulator for complex arrival processes.
problem Modeling and simulating complex arrival processes with non-stationary and multi-dimensional rates.
method Integrates Monte Carlo and GANs to model a broad class of arrival processes.
result Consistent and efficient estimation of the simulator using Wasserstein distance.
Framework uses probabilistic programming for physics simulation in games.
problem Efficiently simulate physics in games using probabilistic programming.
method Combines model-free and model-based approaches to improve efficiency.
result Model outperforms model-free or model-based approaches alone.
In this paper we outline methodology to efficiently simulate (jump) diffusion bridge sample paths without discretisation error. We achieve this by considering the simulation of conditioned (jump) diffusion bridge sample paths in light of recent work developing a mathematical framework for simulating finite dimensional …
Paper proposes a framework for token economy simulation and wealth distribution.
problem Simulation and regulation of token economies.
method Formal analysis framework for tokenomics, defining mechanisms for wealth distribution and stability.
result Algorithmic regulatory controls for token economies to achieve desired wealth distribution.
Quantifying and Attributing Submodel Uncertainty in Stochastic Simulation Models and Digital Twinsstat.CO
The paper quantifies and attributes uncertainty in complex system simulations.
problem Uncertainty in complex system simulations due to unknown or approximated subprocesses.
method Developed a framework for quantifying and attributing submodel uncertainty using bootstrapping, Bayesian model averaging, and tree-based methods.
result Individual submodels contribute to overall uncertainty, and their importance can be quantified.
RoPE framework calibrates misspecified simulators for reliable inference.
problem Misspecification compromises reliability of simulation-based inference.
method Data-driven calibration using optimal transport and a small calibration set.
result RoPE framework improves inference accuracy and uncertainty calibration.
3DB framework tests and debugs computer vision models using photorealistic simulation.
problem Discovering vulnerabilities and understanding model decision-making in computer vision systems.
method Unified framework using photorealistic simulation to test and debug vision models.
result Insights generated by 3DB transfer to the physical world, enabling robustness analysis.
Paper proposes a new framework to improve policy optimization by aligning real and simulated data distributions.
problem Inaccurate model estimation leads to performance degradation in model-based reinforcement learning.
method Introduces unsupervised model adaptation to minimize the IPM between real and simulated data distributions.
result Achieves state-of-the-art performance in sample efficiency on various continuous control tasks.
Unified framework for simulation-based inference learns a single model for multiple tasks.
problem Simulation-based inference for multiple tasks with limited model retraining.
method Unified flow-matching generative model with query-aware masking distribution.
result Competitive performance on various inference tasks and real-world problems.
Can Large Language Models Trade? Testing Financial Theories with LLM Agents in Market Simulationsq-fin.CP
LLMs simulate financial markets, revealing consistent trading strategies and market dynamics.
problem Testing financial theories with AI trading agents.
method Simulated stock market with LLMs using a persistent order book and varied strategies.
result LLMs can simulate different trading strategies and market dynamics.
Understanding the evolution of human society, as a complex adaptive system, is a task that has been looked upon from various angles. In this paper, we simulate an agent-based model with a high enough population tractably. To do this, we characterize an entity called \textit{society}, which helps us reduce the complexit…
USeMOC framework reduces expensive simulations for MO optimization with constraints.
problem Efficiently optimizing multi-objective problems with constraints using expensive function evaluations.
method USeMOC framework uses surrogate models to identify promising candidates and selects the best based on uncertainty.
result USeMOC achieves more than 90% reduction in function evaluations for circuit optimization.
In this paper, we face the problem of simulating discrete random variables with general and varying distributions in a scalable framework, where fully parallelizable operations should be preferred. The new paradigm is inspired by the context of discrete choice models. Compared to classical algorithms, we add paralleliz…
A new Python-C++ framework for agent-based simulation.
problem Understanding market dynamics and effects of delays.
method User-friendly Python API with efficient C++ implementation, message-driven architecture.
result Investigated the role of order processing delay in financial markets.
Study improves accuracy of weather data for real-time building simulations.
problem Anomalous and missing weather data affect real-time building energy simulations.
method Introduces a framework for quality control of measured weather data using anomaly detection and neural network infilling.
result Neural Networks enhance the accuracy of data imputation compared to traditional methods.
Models of complex systems are often formalized as sequential software simulators: computationally intensive programs that iteratively build up probable system configurations given parameters and initial conditions. These simulators enable modelers to capture effects that are difficult to characterize analytically or su…
SGNNs use simulations to train neural networks, improving scientific forecasting and interpretability.
problem Combining precise theory and machine learning for robust scientific modeling.
method Pretraining neural networks on diverse mechanistic simulations as training data.
result SGNNs outperform data-driven and physics-constrained models in forecasting and interpretability.
Simulators enable learning with generalization guarantees in computationally bounded worlds.
problem Generalization in learning theory
method Simulatable processes
result Recovery of PAC learning guarantees with VC dimension
Computationally Efficient CFD Prediction of Bubbly Flow using Physics-Guided Deep Learningphysics.comp-ph
Physics-guided deep learning improves CFD for bubbly flow simulations.
problem Accurate CFD prediction of two-phase bubbly flow with high computational efficiency.
method Developed a multi-scale framework with Feature Similarity Measurement (FSM) for error estimation and a physics-guided deep feedforward neural network (DFNN) surrogate model.
result Physics-guided deep learning achieves comparable accuracy to fine-mesh simulations with fast-running feature.
Study compares methods for detecting novelty in textual data streams.
problem Lack of annotated datasets for novelty detection.
method Simulation framework to create controlled datasets and benchmark methods.
result Evaluation of state-of-the-art methods on different types of novelty.
This paper presents an evaluation framework that attempts to quantify the "degree of realism" of simulated financial time series, whatever the simulation method could be, with the aim of discover unknown characteristics that are not being properly reproduced by such methods in order to improve them. For that purpose, t…
Hybrid analytic and machine-learned baryonic property insertion into galactic dark matter haloesastro-ph.GA
Hybrid model speeds up galaxy simulations by incorporating baryonic properties.
problem Inaccurate baryonic properties in dark matter-only simulations.
method Combining analytic models and machine learning for faster, more accurate simulations.
result Hybrid model outperforms machine learning alone for some baryonic properties.
Bayesian Learning of Conditional Kernel Mean Embeddings for Automatic Likelihood-Free Inferencestat.ML
In likelihood-free settings where likelihood evaluations are intractable, approximate Bayesian computation (ABC) addresses the formidable inference task to discover plausible parameters of simulation programs that explain the observations. However, they demand large quantities of simulation calls. Critically, hyperpara…
Financial Wind Tunnel generates versatile market data for model testing.
problem Inconsistent market dynamics across different scales and sources.
method Retrieval-augmented diffusion-based simulator integrating macro and micro patterns.
result Enhanced performance and adaptability of downstream models in complex markets.
A Spatial Interpolation Framework for Efficient Valuation of Large Portfolios of Variable Annuitiesq-fin.CP
Variable Annuity (VA) products expose insurance companies to considerable risk because of the guarantees they provide to buyers of these products. Managing and hedging these risks requires insurers to find the value of key risk metrics for a large portfolio of VA products. In practice, many companies rely on nested Mon…
In this short note, we show how the parallel adaptive Wang-Landau (PAWL) algorithm of Bornn et al. (2013) can be used to automate and improve simulated tempering algorithms. While Wang-Landau and other stochastic approximation methods have frequently been applied within the simulated tempering framework, this note demo…
Paper introduces a new method for calibrating ESGs to both historical and forward-looking data.
problem Lack of a generally accepted methodology for calibrating ESGs to forward-looking information.
method Conditional Scenario Simulator framework for consistent calibration of economic and financial variables.
result Framework can embed various financial and macroeconomic models and demonstrate practical examples in frequentist and Bayesian settings.
Benchmark of Likelihood-Free Inference Methods based on Neural and Optimal Transport Approachesstat.ME
This study benchmarks likelihood-free inference methods for models with heavy-tailed or discrete data.
problem Comparing likelihood-free inference methods for models with structural features like heavy-tails or discreteness.
method Four approaches: MLE, NBE, EOT, and AW-NBE are evaluated using simulations.
result The choice of evaluation tools is crucial for models with extremes and discrete data.
Simulation-based inference of Bayesian hierarchical models while checking for model misspecificationstat.ME
The paper improves SBI for BHMs by diagnosing misspecification and inferring parameters.
problem Model misspecification in Bayesian hierarchical models.
method Two-step framework: latent function diagnosis followed by SBI of target parameters.
result Improved simulation-based inference for complex models without explicit model checking.
CausalSim corrects bias in trace-driven simulations for more accurate results.
problem Bias in trace-driven simulations due to system conditions during trace collection.
method CausalSim learns a causal model of system dynamics and latent factors from an RCT to remove bias from trace data.
result CausalSim reduces simulation errors by 53% and 61% compared to baselines, providing more accurate insights.