Study uses machine learning to optimize seismic design parameters.
problem Optimizing seismic design parameters for performance-based design.
method Implementing explainable machine learning models to map design variables and performance metrics, integrated into a genetic optimization algorithm.
result Highly accurate surrogate models (R2> 90%) across diverse building types and hazards, identifying optimal member properties.
The paper presents new machine learning methods: signal composition, which classifies time-series regardless of length, type, and quantity; and self-labeling, a supervised-learning enhancement. The paper describes further the implementation of the methods on a financial search engine system using a collection of 7,881 …
Paper uses SGD for solving linear inverse problems, improving empirical performance.
problem Solving statistical inverse problems in science and engineering.
method Stochastic Gradient Descent (SGD) for linear inverse problems, with smoothing techniques.
result Consistency and finite sample bounds for excess risk demonstrated.
This paper tackles real-time Bayesian inverse problems using neural networks.
problem Real-time inference of posterior distributions from experimental data.
method Amortized variational inference with Gaussian and Flow guides.
result The approach provides posterior estimates in real-time at the cost of a forward pass.
A new framework uses an Incremental Transformer to design geopolymer mixtures efficiently.
problem Designing geopolymer mixtures with limited data and physical constraints.
method Topology-aware surrogate framework guided by Incremental Transformer.
result The design space is redundant, with fewer effective mixture regimes.
TNet combines DL with physics models to solve inverse problems efficiently.
problem Solving inverse problems with limited data and physics constraints.
method Model-constrained deep learning approach using TNet.
result TNet solutions are as accurate as traditional methods but faster.
New method uses PINNs to solve complex PDEs with sparse measurements.
problem Joint estimation of source and parameters in advection-diffusion equations with limited data.
method Weighted adaptive approach based on neural tangent kernel of PINNs.
result Successful estimation of source function, velocity, and diffusion parameters.
Enhances inverse design optimization with machine learning and reduced fidelity simulations.
problem Limited compute resources in inverse design optimization.
method Synergy of multi-fidelity simulations, machine learning, and search space reduction.
result Significant computational resource savings and improved optimization performance.
GABI learns geometry from diverse systems to improve Bayesian inference.
problem Bayesian inversion of physical systems with varying geometries.
method Geometric Autoencoders for Bayesian Inversion (GABI) learns geometry-aware priors from large datasets.
result GABI yields comparable predictive accuracy to deterministic methods and well-calibrated uncertainty quantification.
We propose a new approach to inverse reinforcement learning (IRL) based on the deep Gaussian process (deep GP) model, which is capable of learning complicated reward structures with few demonstrations. Our model stacks multiple latent GP layers to learn abstract representations of the state feature space, which is link…
Unified framework for forward and inverse PDE problems in multiphase media.
problem Non-differentiable inverse problems in discrete-valued material fields.
method GenPANIS: Latent-variable generative framework preserving discrete microstructures.
result Unified bidirectional inference with minimal labeled pairs and physics-aware decoder.
Explicit engineering of reward functions for given environments has been a major hindrance to reinforcement learning methods. While Inverse Reinforcement Learning (IRL) is a solution to recover reward functions from demonstrations only, these learned rewards are generally heavily \textit{entangled} with the dynamics of…
Inverse design is an outstanding challenge in disordered systems with multiple length scales such as polymers, particularly when designing polymers with desired phase behavior. We demonstrate high-accuracy tuning of poly(2-oxazoline) cloud point via machine learning. With a design space of four repeating units and a ra…
GeoFunFlow tackles inverse problems on complex geometries with efficient learning.
problem Challenges in inverse problems governed by PDEs, especially on irregular geometries.
method Combines geometric function autoencoder and latent diffusion model trained via rectified flow.
result Achieves state-of-the-art reconstruction accuracy and efficient inference.
Novel method uses deep generative models for efficient Bayesian inverse problem solving.
problem Efficiently solving inverse problems with large, discrete fields and limited prior information.
method Bayesian inference with deep generative models in low-dimensional latent space.
result Accurate and reliable uncertainty estimates for large-scale inverse problems.
RePS improves diffusion models for solving inverse problems efficiently.
problem Solving inverse problems with incomplete or noisy measurements.
method Restart for Posterior Sampling (RePS) using pre-trained diffusion models.
result RePS achieves faster convergence and superior reconstruction quality.
New method improves DMs for solving inverse problems by maximizing conditional mutual information.
problem Efficiently solving noisy linear inverse problems without additional task-specific training.
method Maximizing conditional mutual information between reconstructed signal and measurement.
result Significantly improves the quality of generated images in inverse problems.
Self-supervised methods learn from noisy data alone, useful for imaging problems.
problem Inferring signals from noisy and incomplete observations.
method Learning a solver from measurement data alone, without ground-truth references.
result Self-supervised methods can learn meaningful estimates from noisy data.
This study proposes an efficient surrogate for Darcy flow inverse problems.
problem Efficiently constructing accurate surrogate models for high-dimensional complex inverse problems.
method Sequential Bayesian design strategy to acquire a locally accurate surrogate model focusing on high-probability regions.
result The proposed method accelerates inversion accuracy and computational speed.
Unified Bayesian PINN framework for solving inverse problems in infrared image processing.
problem Solving inverse problems in high-dimensional settings with complex physics.
method Bayesian Physics-Informed Neural Networks (BPINN-IP) framework, incorporating physical laws and uncertainties.
result Unified framework for physical constraints, prior knowledge, and data-driven inference with uncertainty quantification.
Full Wave Inversion (FWI) imaging scheme has many applications in engineering, geoscience and medical sciences. In this paper, a surrogate deep learning FWI approach is presented to quantify properties of materials using stress waves. Such inverse problems, in general, are ill-posed and nonconvex, especially in cases w…
New algorithm speeds up Bayesian UQ for high-dimensional inverse problems.
problem Computational inefficiency in Bayesian inference for high-dimensional inverse problems.
method Deep neural network-based autoencoder for dimension reduction and emulation phase.
result Computational efficiency up to three orders of magnitude with scalable Bayesian UQ.
The paper develops a method to infer model parameters and shared dynamics from related physical systems using data.
problem Calibrating models to match data when detailed system properties and laws are unknown.
method Hierarchical Bayesian framework, adaptive surrogate models, bilevel optimization.
result Joint estimation of individual model parameters and shared dynamics using data from related systems.
Blade uses diffusion priors to accurately and calibratedly infer complex systems.
problem Derivative-free Bayesian inversion for high-dimensional, nonlinear problems with costly forward models.
method Blade employs an ensemble of interacting particles and diffusion models as priors, querying forward models only through evaluations.
result Blade produces well-calibrated posterior samples that existing methods cannot, improving with more iterations and particles.
RID-Noise improves robust design under noisy conditions using neural networks.
problem Design robustness under noisy environments.
method Robust Inverse Design under Noise (RID-Noise) using conditional invertible neural networks (cINNs).
result RID-Noise achieves more effective robust design compared to state-of-the-art methods.
Bayesian method uses deep learning prior for CT reconstruction.
problem Imaging inverse problems in CT reconstruction.
method SA-Roundtrip prior with HMC-pCN sampler.
result Outperforms state-of-the-art methods in CT reconstruction.
New diagnostic method detects misspecified models in inverse PDE problems.
problem Misleading residual-norm diagnostics in inverse PDE problems.
method Structure-sensitive sequential diagnostic using e-processes.
result Rejects fitted models that produce biased predictions.
Paper examines stability of Bayesian posterior measures using integral probability metrics.
problem Stability of Bayesian inference in large-scale inverse problems.
method New families of integral probability metrics for likelihood and prior perturbations.
result Constructs new stability results for Bayesian posterior measures.
Geometric framework explains and controls implicit bias in machine learning.
problem Understanding and controlling the selection of solutions in overparameterized models.
method Developed a theoretical and constructive framework based on geometric corrections induced by gradient noise and continuous symmetries of the loss.
result Computed the induced bias across various architectures and enabled inverse design to shape the bias.
Framework combines machine learning and inverse methods to quantify uncertainties in model parameters.
problem Combining aleatoric and epistemic uncertainties in engineered systems modeling.
method Develops a robust filtering step in LUQ to learn useful QoI maps from noisy datasets, iterates over time, and uses sufficiency tests.
result Transforms datasets into distributions for DC-based inversion, improving parameter quantification.
Paper proposes DC functions for better regularization of inverse problems with theoretical guarantees.
problem Improving regularization for ill-posed inverse problems.
method Introduces difference-of-convex (DC) functions and uses them with optimization algorithms like DCA and PSM.
result DC functions yield improved performance and theoretical guarantees compared to weakly convex functions.
Proposes neural networks for solving complex free boundary problems.
problem Solving free boundary and Stefan problems with complex interfaces.
method Physics-informed neural networks for approximating solutions and boundaries.
result Successfully approximates solutions and moving boundaries in various Stefan problems.
Study identifies unique minimizers for interaction kernels in particle systems.
problem Identifying unique interaction kernels in mean-field equations of interacting particles.
method Data-adaptive L2 spaces, RKHS analysis, regularization. result Characterization of identifiability in both finite and infinite particle systems.
DAISI improves data assimilation for complex systems with noisy observations.
problem Limited accuracy of classical DA methods in complex, nonlinear systems.
method Generative models with inverse sampling for flexible probabilistic inference.
result DAISI achieves accurate filtering results in challenging nonlinear systems.
New method learns optimal variance schedule for diffusion models.
problem Diffusion models' sensitivity to variance schedule.
method Probabilistic conditioning, learning schedule during training.
result Comparable or superior results in super-resolution microscopy and quantitative phase imaging.
This research improves neural likelihood approximation for Bayesian inverse problems.
problem Challenges in modeling and inference for high-dimensional Bayesian inverse problems.
method Develops a strictly convex approximation framework for neural likelihood.
result Empirical minimizers converge to the true likelihood as sample size increases.
RADIS uses deep regression to create efficient importance sampling for model inversion and emulation.
problem Efficiently sampling from posterior distributions for model inversion and emulation.
method RADIS uses a deep architecture of nested importance sampling schemes to construct a non-parametric emulator that mimics the posterior distribution.
result RADIS asymptotically converges to an exact sampler under mild conditions and can be used as a surrogate model.
New approach uses secants to improve sensor placement and feature selection for nonlinear systems.
problem Inadequacy of linear methods for minimal sensor placement and feature selection in nonlinear systems.
method Data-driven approach using secant vectors to develop greedy algorithms for robust, near-minimal reconstruction guarantees.
result Demonstrated on two problems where linear techniques fail, secant-based approach provides robust solutions.
Microfluidic devices are utilized to control and direct flow behavior in a wide variety of applications, particularly in medical diagnostics. A particularly popular form of microfluidics -- called inertial microfluidic flow sculpting -- involves placing a sequence of pillars to controllably deform an initial flow field…
PIED optimizes experimental design for inverse problems using physics-informed neural networks.
problem Optimizing experimental design for inverse problems with limited budget and constraints.
method PIED uses physics-informed neural networks (PINNs) for continuous optimization of design parameters in one-shot deployments.
result PIED significantly outperforms existing ED methods in solving inverse problems, including unknown functions.
AI-driven framework optimizes MCMC-based preconditioners for faster linear system solving.
problem Slow convergence of Krylov subspace solvers for ill-conditioned matrices.
method Graph neural surrogate and Bayesian optimization for AI-tuned MCMC parameters.
result 50% reduction in iterations to convergence on unseen system.
Paper introduces active and passive causal inference techniques.
problem Causal inference in machine learning.
method Categorizes causal inference techniques into active and passive approaches.
result Describes and discusses various causal inference methods.
New method generates clean data from corrupted observations.
problem Generating clean data from corrupted observations.
method Iterative update of a transport map using black-box corruption channel access.
result Converges to a self-consistent transport map that effectively inverts the corruption channel.
Reinforcement learning is a promising framework for solving control problems, but its use in practical situations is hampered by the fact that reward functions are often difficult to engineer. Specifying goals and tasks for autonomous machines, such as robots, is a significant challenge: conventionally, reward function…
Advances in deep generative networks have led to impressive results in recent years. Nevertheless, such models can often waste their capacity on the minutiae of datasets, presumably due to weak inductive biases in their decoders. This is where graphics engines may come in handy since they abstract away low-level detail…
A new estimator reduces variance in slate bandit OPE.
problem Large action spaces in slate bandits cause high variance in OPE.
method Develops Latent IPS (LIPS) to optimize slate abstractions for low variance and bias.
result LIPS substantially outperforms existing estimators in scenarios with non-linear rewards and large slate spaces.
We present evidence that the best model for empirical volume-price distributions is not always the same and it strongly depends in (i) the region of the volume-price spectrum that one wants to model and (ii) the period in time that is being modelled. To show these two features we analyze stocks of the New York stock ma…
LUQ learns QoI from dynamical systems for consistent observation inversion.
problem Quantifying uncertainties on model inputs corresponding to observable QoI in dynamical systems.
method LUQ framework for SIPs, including data filtering, dynamics learning, observation classification, and feature extraction.
result LUQ provides tractable solutions to SIPs for dynamical systems, enabling uncertainty quantification.