On-device research index

arXiv research

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.

168,742 papers · 148 categories

Trend · papers per month

count % of corpus

6 results for “bi-fidelity”

Neural Network Training Using

\ell_1

-Regularization and Bi-fidelity Datastat.ML

New neural network training method uses bi-fidelity data to reduce errors.

problem Training neural networks with limited high-fidelity data.

method Bi-fidelity

\ell_1

-regularization strategies.

result Bi-fidelity

\ell_1

-regularization reduces errors by one order of magnitude.

neural networks regularization bi-fidelity sparsity uncertainty propagation

2021-05-27abs ↗pdf ↗

Bi-fidelity Variational Auto-encoder for Uncertainty Quantificationstat.ML

BF-VAE estimates uncertainty from LF and HF QoI samples.

problem Balancing computational efficiency and numerical accuracy in uncertainty quantification.

method Bi-fidelity formulation of VAEs in latent space.

result BF-VAE improves accuracy with limited HF data.

uncertainty quantification variational auto-encoder bi-fidelity information bottleneck quantitative modeling

2023-05-25abs ↗pdf ↗

On transfer learning of neural networks using bi-fidelity data for uncertainty propagationstat.ML

Paper explores using bi-fidelity data to train neural networks for uncertainty quantification.

problem Training neural networks requires large amounts of data, which may not be available for computationally expensive systems.

method Transfer learning techniques using high- and low-fidelity models, including standard transfer and bi-fidelity weighted learning.

result Bi-fidelity transfer learning improves accuracy over standard training approaches.

transfer learning neural networks uncertainty quantification bi-fidelity neural surrogate

2020-02-11abs ↗pdf ↗

Bi-fidelity Modeling of Uncertain and Partially Unknown Systems using DeepONetsstat.ML

DeepONet models system discrepancies with low data.

problem Modeling complex systems with limited data.

method Bi-fidelity modeling using DeepONet for uncertain and partially unknown systems.

result DeepONet effectively models complex systems with parametric uncertainty and partial unknownness.

DeepONet bi-fidelity modeling complex systems uncertainty partial unknownness

2022-04-03abs ↗pdf ↗

A Bi-fidelity DeepONet Approach for Modeling Uncertain and Degrading Hysteretic Systemsstat.ML

Paper uses deep learning to model systems with degrading behavior.

problem Modeling systems with degrading hysteretic behavior and uncertainty.

method Uses low-fidelity data to train a deep operator network (DeepONet).

result Improves prediction error in degrading hysteretic systems with uncertainty.

deep learning hysteretic systems uncertainty deepONet degradation

2023-04-25abs ↗pdf ↗

Computing Black Scholes with Uncertain Volatility-A Machine Learning Approachq-fin.MF

A machine learning approach to compute Black-Scholes prices with uncertain volatility.

problem Approximating financial markets with continuous-time models like Black-Scholes when data is discrete.

method Generalized Polynomial Chaos (gPC) method combined with a machine learning technique called Bi-Fidelity.

result Efficient numerical method to quantify uncertainty in derivative pricing.

Black-Scholes uncertain volatility machine learning Polynomial Chaos gPC

2022-02-15abs ↗pdf ↗