Psoriasis

SpinCastML an Open Decision-Making Application for Inverse Design of Electrospinning Manufacturing: A Machine Learning, Optimal Sampling and Inverse Monte Carlo Approach

Electrospinning is a powerful technique for producing micro to nanoscale fibers with application specific architectures. Small variations in solution or operating conditions can shift the jet regime, generating non Gaussian fiber diameter distributions. Despite substantial progress, no existing framework enables inverse design toward desired fiber outcomes while integrating polymer solvent chemica...

Trajectory Stitching for Solving Inverse Problems with Flow-Based Models

Flow-based generative models have emerged as powerful priors for solving inverse problems. One option is to directly optimize the initial latent code (noise), such that the flow output solves the inverse problem. However, this requires backpropagating through the entire generative trajectory, incurring high memory costs and numerical instability. We propose MS-Flow, which represents the trajectory...

Recovering 3D Shapes from Ultra-Fast Motion-Blurred Images

We consider the problem of 3D shape recovery from ultra-fast motion-blurred images. While 3D reconstruction from static images has been extensively st...

Modeling the inverse MEG problem in neuro-imaging using Physics Informed Neural Networks

Magnetoencephalography (MEG) forward and inverse modeling is fundamental to neuroscientific discovery, yet the inversion of partial differential equat...

Bayesian PINNs for uncertainty-aware inverse problems (BPINN-IP)

The main contribution of this paper is to develop a hierarchical Bayesian formulation of PINNs for linear inverse problems, which is called BPINN-IP. ...

Score-based diffusion models for diffuse optical tomography with uncertainty quantification

Score-based diffusion models are a recently developed framework for posterior sampling in Bayesian inverse problems with a state-of-the-art performanc...

End-to-end reconstruction of OCT optical properties and speckle-reduced structural intensity via physics-based learning

Inverse scattering in optical coherence tomography (OCT) seeks to recover both structural images and intrinsic tissue optical properties, including re...

On Stability and Robustness of Diffusion Posterior Sampling for Bayesian Inverse Problems

Diffusion models have recently emerged as powerful learned priors for Bayesian inverse problems (BIPs). Diffusion-based solvers rely on a presumed lik...

LightCity: An Urban Dataset for Outdoor Inverse Rendering and Reconstruction under Multi-illumination Conditions

Inverse rendering in urban scenes is pivotal for applications like autonomous driving and digital twins. Yet, it faces significant challenges due to c...

SSNAPS: Audio-Visual Separation of Speech and Background Noise with Diffusion Inverse Sampling

This paper addresses the challenge of audio-visual single-microphone speech separation and enhancement in the presence of real-world environmental noi...

Solving Inverse Problems with Flow-based Models via Model Predictive Control

Flow-based generative models provide strong unconditional priors for inverse problems, but guiding their dynamics for conditional generation remains c...

Expected Return Causes Outcome-Level Mode Collapse in Reinforcement Learning and How to Fix It with Inverse Probability Scaling

Many reinforcement learning (RL) problems admit multiple terminal solutions of comparable quality, where the goal is not to identify a single optimum ...

The Ensemble Inverse Problem: Applications and Methods

We introduce a new multivariate statistical problem that we refer to as the Ensemble Inverse Problem (EIP). The aim of EIP is to invert for an ensembl...

Contrast-Source-Based Physics-Driven Neural Network for Inverse Scattering Problems

Deep neural networks (DNNs) have recently been applied to inverse scattering problems (ISPs) due to their strong nonlinear mapping capabilities. Howev...

Geometry-Free Conditional Diffusion Modeling for Solving the Inverse Electrocardiography Problem

This paper proposes a data-driven model for solving the inverse problem of electrocardiography, the mathematical problem that forms the basis of elect...

GR3EN: Generative Relighting for 3D Environments

We present a method for relighting 3D reconstructions of large room-scale environments. Existing solutions for 3D scene relighting often require solvi...

Multi-objective fluorescent molecule design with a data-physics dual-driven generative framework

Designing fluorescent small molecules with tailored optical and physicochemical properties requires navigating vast, underexplored chemical space whil...

Likelihood-Separable Diffusion Inference for Multi-Image MRI Super-Resolution

Diffusion models are the current state-of-the-art for solving inverse problems in imaging. Their impressive generative capability allows them to appro...

Soft Shadow Diffusion (SSD): Physics-inspired Learning for 3D Computational Periscopy

Conventional imaging requires a line of sight to create accurate visual representations of a scene. In certain circumstances, however, obtaining a sui...

Inverse Rendering for High-Genus 3D Surface Meshes from Multi-view Images with Persistent Homology Priors

Reconstructing 3D objects from images is inherently an ill-posed problem due to ambiguities in geometry, appearance, and topology. This paper introduc...