CRUNCH Group: Home of Math + Machine Learning + X

@crunchgroup

45 tuit · page 1/2

Formal & Physical Sciences AI — Models & Research

Digital twins modeling via conditional generative models

Zongren Zou

August 14, 2026 55 min

★ ★ ★ ★ ☆ 4/5

Digital TwinsConditional Generative ModelsStructure Learning

Formal & Physical Sciences AI — Models & Research

KANDy: Kolmogorov-Arnold Networks for Dynamics and Emerging Variants

Dr. Kevin Slote

August 7, 2026 56 min

★ ★ ★ ★ ☆ 4/5

Kolmogorov-Arnold NetworksDynamical SystemsSparse Regression

Formal & Physical Sciences AI — Models & Research

Neural Network-Based Estimation of Time-Dependent Parameters in AR(p) Processes

Agnieszka Kopeć and Martyna Wiącek

July 31, 2026 57 min

★ ★ ★ ★ ☆ 4/5

Neural NetworksTime SeriesParameter Estimation

Applied Sciences & Engineering AI — Models & Research

Symmetry-Preserving Compilers || Adaptive Spectral & Low-Rank Representations NOs|| July 24, 2026

CRUNCH Group: Home of Math + Machine Learning + X

July 24, 2026 122 min

★ ★ ★ ☆ ☆ 3/5

Symplectic IntegrationNeural OperatorsEdge Computing

Applied Sciences & Engineering Mathematics

Neural-operator element method

Weihang Ouyang

July 17, 2026 61 min

★ ★ ★ ★ ☆ 4/5

Neural OperatorsFinite Element MethodPDEs

Formal & Physical Sciences AI — Models & Research

Resource-efficient Foundation Models for Scientific Applications

Nils Thuerey

July 12, 2026 71 min

★ ★ ★ ★ ☆ 4/5

Foundation ModelsScientific ComputingPDEs

Formal & Physical Sciences AI — Models & Research

Physics-Constrained Agentic Training of Entropy-Stable PINNs

Dr. Khalil Haddaoui

June 26, 2026 58 min

★ ★ ★ ★ ☆ 4/5

PINNsHyperbolic Conservation LawsEntropy Stability

Formal & Physical Sciences AI — Models & Research

When PINNs Go Wrong: Pseudo-Time Stepping Against Spurious Solutions

Sifan Wang

June 13, 2026 68 min

★ ★ ★ ★ ☆ 4/5

Physics-Informed Neural NetworksPseudo-Time SteppingSpurious Solutions

Applied Sciences & Engineering AI — Models & Research

Diffusion Models for Probabilistic Forecasting

Hojin Kim

June 5, 2026 57 min

★ ★ ★ ★ ☆ 4/5

Diffusion ModelsTurbulent FlowProbabilistic Forecasting

Formal & Physical Sciences AI — Models & Research

ϕ−DeepONet: A Discontinuity Capturing Neural Operator

Sumanta Roy

May 29, 2026 57 min

★ ★ ★ ★ ☆ 4/5

Neural OperatorsPhysics-InformedDiscontinuities

Formal & Physical Sciences AI — Models & Research

In-context modeling as a retrain-free paradigm for foundation models

Yang Liu

May 22, 2026 46 min

★ ★ ★ ★ ☆ 4/5

In-Context LearningFoundation ModelsComputational Science

Formal & Physical Sciences AI — Models & Research

Stochastic Port-Hamiltonian Neural Networks

Youness Outaleb

May 15, 2026 86 min

★ ★ ★ ★ ☆ 4/5

Stochastic Port-Hamiltonian SystemsNeural NetworksPassivity

Formal & Physical Sciences Mathematics

Reduced order models for parameterized PDEs|| Neural network chemical kinetics || May 8, 2026

CRUNCH Group: Home of Math + Machine Learning + X

May 8, 2026 136 min

★ ★ ★ ★ ☆ 4/5

Reduced Order ModelsDynamic Mode DecompositionNeural Networks

Formal & Physical Sciences AI — Models & Research

A case study on AI+math || pADAM for Multi-Physics Learning || Apr 24, 2026

CRUNCH Group: Home of Math + Machine Learning + X

April 24, 2026 113 min

★ ★ ★ ★ ☆ 4/5

AI MathematicsErdős ProblemMulti-Physics

Formal & Physical Sciences AI — Models & Research

Ultra Fast PDE Solving via Physics Guided Few-step Diffusion

Xiangrui Kong

April 17, 2026 74 min

★ ★ ★ ★ ☆ 4/5

Diffusion ModelsPDE SolvingDistillation

Formal & Physical Sciences AI — Models & Research

Neural Pushforward Samplers || NN for Nonlinear Hyperbolic Conservation Laws|| Apr 10, 2026

CRUNCH Group: Home of Math + Machine Learning + X

April 10, 2026 114 min

★ ★ ★ ★ ☆ 4/5

Fokker-PlanckNeural NetworksPartial Differential Equations

Formal & Physical Sciences Mathematics

Convergence Analysis of Consistent PINNs for Elliptic PDEs

Jonathan W. Siegel

April 3, 2026 72 min

★ ★ ★ ★ ☆ 4/5

PINNsElliptic PDEsConvergence Analysis

Formal & Physical Sciences AI — Models & Research

Stochastic PINNs solvers|| Multi-Resolution Independent Simulations for Turbulence || March 27, 2026

CRUNCH Group: Home of Math + Machine Learning + X

March 27, 2026 118 min

★ ★ ★ ★ ☆ 4/5

Stochastic Differential EquationsPhysics-Informed Neural NetworksTurbulence Simulation

Applied Sciences & Engineering AI — Models & Research

PINNs for Blood Flow Simulation|| ZENN for Heterogeneous Data-Driven Modeling || March 20, 2026

CRUNCH Group: Home of Math + Machine Learning + X

March 20, 2026 139 min

★ ★ ★ ★ ☆ 4/5

Physics-Informed Neural NetworksBlood Flow SimulationZENN

Formal & Physical Sciences AI — Models & Research

Physics-Informed Laplace Neural Operators || ML linear algebra algorithms || March 13, 2026

Heechang Kim, Michael Mahoney

March 13, 2026 127 min

★ ★ ★ ★ ☆ 4/5

Neural OperatorsPhysics-InformedPDE

Formal & Physical Sciences AI — Models & Research

Multi-physics Joint Inversion || Learning perturbative nonlinear oscillatory dynamics||March 6, 2026

CRUNCH Group: Home of Math + Machine Learning + X

March 6, 2026 102 min

★ ★ ★ ★ ☆ 4/5

Multi-Physics InversionDeep LearningPhysics-Informed AI

Formal & Physical Sciences Mathematics

Dirichlet boundary conditions on convex polygonal domains

Prof. N. Sukumar and Dr. Ritwick Roy

February 27, 2026 75 min

★ ★ ★ ★ ☆ 4/5

Physics-Informed Neural NetworksWachspress CoordinatesTransfinite Interpolation

Formal & Physical Sciences AI — Models & Research

Why Muon Is Good but May Not Be Optimal

Weijie Su

February 20, 2026 60 min

★ ★ ★ ★ ☆ 4/5

MuonOptimizationDeep Learning

Formal & Physical Sciences AI — Models & Research

Randomized Greedy Algorithms for Neural Network Optimization

Xiaofeng Xu

February 13, 2026 64 min

★ ★ ★ ★ ☆ 4/5

Greedy AlgorithmsNeural NetworksPartial Differential Equations

Formal & Physical Sciences AI — Models & Research

Generative Models for Physics-Based Control || Thermodynamics-Driven Learning || Feb 6, 2026

CRUNCH Group: Home of Math + Machine Learning + X

February 6, 2026 118 min

★ ★ ★ ★ ☆ 4/5

Generative ModelsPhysics-Based ControlVideo Generation

Formal & Physical Sciences AI — Models & Research

PAINT: Parallel-in-time Neural Twins for Dynamical System Reconstruction

Andreas Radler and Vincent Seyfried

January 30, 2026 73 min

★ ★ ★ ★ ☆ 4/5

Neural TwinsDynamical SystemsParallel-in-Time

Formal & Physical Sciences AI — News & Applications

Multi-Agent Systems for Discovery and Design|| Multi-Modal LLM for Material Science || Jan 23, 2026

CRUNCH Group: Home of Math + Machine Learning + X

January 23, 2026 131 min

★ ★ ★ ★ ☆ 4/5

Multi-Agent SystemsMaterials ScienceLarge Language Models

Formal & Physical Sciences AI — Models & Research

Ultra-Fast Language Generation || Hybrid twinning using PBDW and DeepONet || Jan 16, 2026

CRUNCH Group: Home of Math + Machine Learning + X

January 16, 2026 101 min

★ ★ ★ ★ ☆ 4/5

Diffusion Language ModelsDistillationState Estimation

Formal & Physical Sciences AI — Models & Research

Conditional Gaussian Koopman Network || PINNs with Fourier-enhanced Features || Jan 9, 2026

CRUNCH Group: Home of Math + Machine Learning + X

January 9, 2026 117 min

★ ★ ★ ★ ☆ 4/5

Koopman OperatorData AssimilationPhysics-Informed Neural Networks

Formal & Physical Sciences AI — Models & Research

Transformer-Inspired Physics-Informed DeepONet|| From RoPINN to ProPINN ||Dec 19, 2025

CRUNCH Group: Home of Math + Machine Learning + X

December 19, 2025 115 min

★ ★ ★ ★ ☆ 4/5

Physics-Informed Neural NetworksDeep Operator NetworksTransformer

Formal & Physical Sciences AI — Models & Research

PINN for Challenging Multiphase Flow Problems||Multi-Agent LLM for Intelligent Design ||Dec 12, 2025

CRUNCH Group: Home of Math + Machine Learning + X

December 12, 2025 128 min

★ ★ ★ ★ ☆ 4/5

Physics-Informed Neural NetworksMultiphase FlowMulti-Agent LLM

Formal & Physical Sciences Mathematics

Residual-Based Adaptivity in Neural PDE Solvers || Variance estimation for UQ || Dec 5, 2025

CRUNCH Group: Home of Math + Machine Learning + X

December 5, 2025 115 min

★ ★ ★ ★ ☆ 4/5

Neural PDE SolversResidual-Based AdaptivityUncertainty Quantification

Formal & Physical Sciences AI — Models & Research

Generative AI for Discovering Novel Materials || Semi-analytical NN || Nov 21, 2025

CRUNCH Group: Home of Math + Machine Learning + X

November 21, 2025 97 min

★ ★ ★ ★ ☆ 4/5

Generative AIMaterials DiscoveryCrystal Diffusion VAE

Formal & Physical Sciences AI — Models & Research

Understanding PINN’s Failure Modes || Progressive Sharpening from Jacobian Alignment|| Nov 14, 2025

CRUNCH Group: Home of Math + Machine Learning + X

November 14, 2025 116 min

★ ★ ★ ★ ☆ 4/5

Physics-Informed Neural NetworksOptimizationNeural Network Training

Formal & Physical Sciences AI — Models & Research

Shape-informed Operator Learning || Separable PINNs|| Nov 7, 2025

CRUNCH Group: Home of Math + Machine Learning + X

November 7, 2025 121 min

★ ★ ★ ★ ☆ 4/5

Operator LearningPhysics-Informed Neural NetworksScientific Machine Learning

Formal & Physical Sciences AI — Models & Research

Differential Equations as Neural Network Representations

Adeel Pervez

October 31, 2025 57 min

★ ★ ★ ★ ☆ 4/5

Neural NetworksDifferential EquationsMechanistic Models

Formal & Physical Sciences AI — Models & Research

Causality-Based Learning || Extreme Event Aware (η-) Learning || Oct 24, 2025

CRUNCH Group: Home of Math + Machine Learning + X

October 24, 2025 120 min

★ ★ ★ ★ ☆ 4/5

CausalityMachine LearningData Assimilation

Formal & Physical Sciences AI — Models & Research

Stabilizing PDE-ML system || How Does Neural Network Training Work || Oct 17, 2025

CRUNCH Group: Home of Math + Machine Learning + X

October 17, 2025 140 min

★ ★ ★ ★ ☆ 4/5

PDE-MLNeural Network TrainingEdge of Stability

Formal & Physical Sciences AI — Models & Research

Kernelization of Natural Gradient Methods for PIML

Nilo Schwencke

October 10, 2025 73 min

★ ★ ★ ★ ☆ 4/5

PINNsNatural GradientNeural Tangent Kernel

Formal & Physical Sciences Chemistry

Bayesian Potential for UQ in Chemistry || Oct 3, 2025

CRUNCH Group: Home of Math + Machine Learning + X

October 3, 2025 62 min

★ ★ ★ ★ ☆ 4/5

Bayesian InferenceGraph Neural NetworksUncertainty Quantification